diff --git a/module3/exo3/SARS-CoV-2_analysis.ipynb b/module3/exo3/SARS-CoV-2_analysis.ipynb index a679219cd3be1d44c01c9ee7e748186001fce8f6..602f4b75ce84b3eb811711967a2cabd54eaf17cd 100644 --- a/module3/exo3/SARS-CoV-2_analysis.ipynb +++ b/module3/exo3/SARS-CoV-2_analysis.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -43,9 +43,1784 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
0NaNAfghanistan33.93911067.709953000000...209322209340209358209362209369209390209406209436209451209451
1NaNAlbania41.15330020.168300000000...334391334408334408334427334427334427334427334427334443334457
2NaNAlgeria28.0339001.659600000000...271441271448271463271469271469271477271477271490271494271496
3NaNAndorra42.5063001.521800000000...47866478754787547875478754787547875478754789047890
4NaNAngola-11.20270017.873900000000...105255105277105277105277105277105277105277105277105288105288
5NaNAntarctica-71.94990023.347000000000...11111111111111111111
6NaNAntigua and Barbuda17.060800-61.796400000000...9106910691069106910691069106910691069106
7NaNArgentina-38.416100-63.616700000000...10044125100441251004412510044125100441251004412510044957100449571004495710044957
8NaNArmenia40.06910045.038200000000...446819446819446819446819446819446819446819446819447308447308
9Australian Capital TerritoryAustralia-35.473500149.012400000000...232018232018232619232619232619232619232619232619232619232974
10New South WalesAustralia-33.868800151.209300000034...3900969390096939081293908129390812939081293908129390812939081293915992
11Northern TerritoryAustralia-12.463400130.845600000000...104931104931105021105021105021105021105021105021105021105111
12QueenslandAustralia-27.469800153.025100000000...1796633179663318002361800236180023618002361800236180023618002361800236
13South AustraliaAustralia-34.928500138.600700000000...880207880207881911881911881911881911881911881911881911883620
14TasmaniaAustralia-42.882100147.327200000000...286264286264286264286897286897286897286897286897286897287507
15VictoriaAustralia-37.813600144.963100000011...2874262287426228772602877260287726028772602877260287726028772602880559
16Western AustraliaAustralia-31.950500115.860500000000...1291077129107712934611293461129346112934611293461129346112934611293461
17NaNAustria47.51620014.550100000000...5911294591961659261485931247593666659409355943417594941859558605961143
18NaNAzerbaijan40.14310047.576900000000...828548828588828628828648828682828721828730828783828819828825
19NaNBahamas25.025885-78.035889000000...37491374913749137491374913749137491374913749137491
20NaNBahrain26.02750050.550000000000...707480707828708061708532708768709230709230709858710306710693
21NaNBangladesh23.68500090.356300000000...2037773203782920378292037829203782920378292037829203782920378712037871
22NaNBarbados13.193900-59.543200000000...106645106645106645106645106645106645106645106645106645106798
23NaNBelarus53.70980027.953400000000...994037994037994037994037994037994037994037994037994037994037
24NaNBelgium50.8333004.469936000000...4717655471765547277954727795472779547277954727795472779547277954739365
25NaNBelize17.189900-88.497600000000...70757707577075770757707577075770757707577075770757
26NaNBenin9.3077002.315800000000...27990279902799027990279902799027990279992799927999
27NaNBhutan27.51420090.433600000000...62615626206262062620626206262062620626206262762627
28NaNBolivia-16.290200-63.588700000000...1193009119325611934181193650119381511939081193970119406911941871194277
29NaNBosnia and Herzegovina43.91590017.679100000000...401575401636401636401636401636401636401636401636401729401729
..................................................................
259NaNTuvalu-7.109500177.649300000000...2805280528052805280528052805280528052805
260NaNUS40.000000-100.000000112255...103443455103533872103589757103648690103650837103646975103655539103690910103755771103802702
261NaNUganda1.37333332.290275000000...170504170504170504170504170504170504170504170504170544170544
262NaNUkraine48.37940031.165600000000...5693846570124957013335701474570160257017435701855570195957118185711929
263NaNUnited Arab Emirates23.42407653.847818000000...1051998105212210522471052382105251910526641052664105292610530681053213
264AnguillaUnited Kingdom18.220600-63.068600000000...3904390439043904390439043904390439043904
265BermudaUnited Kingdom32.307800-64.750500000000...18799188141881418814188141881418814188141882818828
266British Virgin IslandsUnited Kingdom18.420700-64.640000000000...7305730573057305730573057305730573057305
267Cayman IslandsUnited Kingdom19.313300-81.254600000000...31472314723147231472314723147231472314723147231472
268Channel IslandsUnited Kingdom49.372300-2.364400000000...0000000000
269Falkland Islands (Malvinas)United Kingdom-51.796300-59.523600000000...1930193019301930193019301930193019301930
270GibraltarUnited Kingdom36.140800-5.353600000000...20423204232042320433204332043320433204332043320433
271GuernseyUnited Kingdom49.448196-2.589490000000...34867349293492934929349293492934929349293499134991
272Isle of ManUnited Kingdom54.236100-4.548100000000...38008380083800838008380083800838008380083800838008
273JerseyUnited Kingdom49.213800-2.135800000000...66391663916639166391663916639166391663916639166391
274MontserratUnited Kingdom16.742498-62.187366000000...1403140314031403140314031403140314031403
275Pitcairn IslandsUnited Kingdom-24.376800-128.324200000000...4444444444
276Saint Helena, Ascension and Tristan da CunhaUnited Kingdom-7.946700-14.355900000000...2166216621662166216621662166216621662166
277Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...6551655165516551655165516551655765576561
278NaNUnited Kingdom55.378100-3.436000000000...24370150243701502439653024396530243965302439653024396530243965302439653024425309
279NaNUruguay-32.522800-55.765800000000...1034303103430310343031034303103430310343031034303103430310343031034303
280NaNUzbekistan41.37749164.585262000000...250932251071251071251071251071251071251071251071251247251247
281NaNVanuatu-15.376700166.959200000000...12014120141201412014120141201412014120141201412014
282NaNVenezuela6.423800-66.589700000000...551981551986551986552014552051552051552125552157552157552162
283NaNVietnam14.058324108.277199022222...11526917115269261152693711526950115269621152696611526966115269861152699411526994
284NaNWest Bank and Gaza31.95220035.233200000000...703228703228703228703228703228703228703228703228703228703228
285NaNWinter Olympics 202239.904200116.407400000000...535535535535535535535535535535
286NaNYemen15.55272748.516388000000...11945119451194511945119451194511945119451194511945
287NaNZambia-13.13389727.849332000000...343012343012343079343079343079343135343135343135343135343135
288NaNZimbabwe-19.01543829.154857000000...263921264127264127264127264127264127264127264127264276264276
\n", + "

289 rows × 1147 columns

\n", + "
" + ], + "text/plain": [ + " Province/State Country/Region \\\n", + "0 NaN Afghanistan \n", + "1 NaN Albania \n", + "2 NaN Algeria \n", + "3 NaN Andorra \n", + "4 NaN Angola \n", + "5 NaN Antarctica \n", + "6 NaN Antigua and Barbuda \n", + "7 NaN Argentina \n", + "8 NaN Armenia \n", + "9 Australian Capital Territory Australia \n", + "10 New South Wales Australia \n", + "11 Northern Territory Australia \n", + "12 Queensland Australia \n", + "13 South Australia Australia \n", + "14 Tasmania Australia \n", + "15 Victoria Australia \n", + "16 Western Australia Australia \n", + "17 NaN Austria \n", + "18 NaN Azerbaijan \n", + "19 NaN Bahamas \n", + "20 NaN Bahrain \n", + "21 NaN Bangladesh \n", + "22 NaN Barbados \n", + "23 NaN Belarus \n", + "24 NaN Belgium \n", + "25 NaN Belize \n", + "26 NaN Benin \n", + "27 NaN Bhutan \n", + "28 NaN Bolivia \n", + "29 NaN Bosnia and Herzegovina \n", + ".. ... ... \n", + "259 NaN Tuvalu \n", + "260 NaN US \n", + "261 NaN Uganda \n", + "262 NaN Ukraine \n", + "263 NaN United Arab Emirates \n", + "264 Anguilla United Kingdom \n", + "265 Bermuda United Kingdom \n", + "266 British Virgin Islands United Kingdom \n", + "267 Cayman Islands United Kingdom \n", + "268 Channel Islands United Kingdom \n", + "269 Falkland Islands (Malvinas) United Kingdom \n", + "270 Gibraltar United Kingdom \n", + "271 Guernsey United Kingdom \n", + "272 Isle of Man United Kingdom \n", + "273 Jersey United Kingdom \n", + "274 Montserrat United Kingdom \n", + "275 Pitcairn Islands United Kingdom \n", + "276 Saint Helena, Ascension and Tristan da Cunha United Kingdom \n", + "277 Turks and Caicos Islands United Kingdom \n", + "278 NaN United Kingdom \n", + "279 NaN Uruguay \n", + "280 NaN Uzbekistan \n", + "281 NaN Vanuatu \n", + "282 NaN Venezuela \n", + "283 NaN Vietnam \n", + "284 NaN West Bank and Gaza \n", + "285 NaN Winter Olympics 2022 \n", + "286 NaN Yemen \n", + "287 NaN Zambia \n", + "288 NaN Zimbabwe \n", + "\n", + " Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", + "0 33.939110 67.709953 0 0 0 0 0 \n", + "1 41.153300 20.168300 0 0 0 0 0 \n", + "2 28.033900 1.659600 0 0 0 0 0 \n", + "3 42.506300 1.521800 0 0 0 0 0 \n", + "4 -11.202700 17.873900 0 0 0 0 0 \n", + "5 -71.949900 23.347000 0 0 0 0 0 \n", + "6 17.060800 -61.796400 0 0 0 0 0 \n", + "7 -38.416100 -63.616700 0 0 0 0 0 \n", + "8 40.069100 45.038200 0 0 0 0 0 \n", + "9 -35.473500 149.012400 0 0 0 0 0 \n", + "10 -33.868800 151.209300 0 0 0 0 3 \n", + "11 -12.463400 130.845600 0 0 0 0 0 \n", + "12 -27.469800 153.025100 0 0 0 0 0 \n", + "13 -34.928500 138.600700 0 0 0 0 0 \n", + "14 -42.882100 147.327200 0 0 0 0 0 \n", + "15 -37.813600 144.963100 0 0 0 0 1 \n", + "16 -31.950500 115.860500 0 0 0 0 0 \n", + "17 47.516200 14.550100 0 0 0 0 0 \n", + "18 40.143100 47.576900 0 0 0 0 0 \n", + "19 25.025885 -78.035889 0 0 0 0 0 \n", + "20 26.027500 50.550000 0 0 0 0 0 \n", + "21 23.685000 90.356300 0 0 0 0 0 \n", + "22 13.193900 -59.543200 0 0 0 0 0 \n", + "23 53.709800 27.953400 0 0 0 0 0 \n", + "24 50.833300 4.469936 0 0 0 0 0 \n", + "25 17.189900 -88.497600 0 0 0 0 0 \n", + "26 9.307700 2.315800 0 0 0 0 0 \n", + "27 27.514200 90.433600 0 0 0 0 0 \n", + "28 -16.290200 -63.588700 0 0 0 0 0 \n", + "29 43.915900 17.679100 0 0 0 0 0 \n", + ".. ... ... ... ... ... ... ... \n", + "259 -7.109500 177.649300 0 0 0 0 0 \n", + "260 40.000000 -100.000000 1 1 2 2 5 \n", + "261 1.373333 32.290275 0 0 0 0 0 \n", + "262 48.379400 31.165600 0 0 0 0 0 \n", + "263 23.424076 53.847818 0 0 0 0 0 \n", + "264 18.220600 -63.068600 0 0 0 0 0 \n", + "265 32.307800 -64.750500 0 0 0 0 0 \n", + "266 18.420700 -64.640000 0 0 0 0 0 \n", + "267 19.313300 -81.254600 0 0 0 0 0 \n", + "268 49.372300 -2.364400 0 0 0 0 0 \n", + "269 -51.796300 -59.523600 0 0 0 0 0 \n", + "270 36.140800 -5.353600 0 0 0 0 0 \n", + "271 49.448196 -2.589490 0 0 0 0 0 \n", + "272 54.236100 -4.548100 0 0 0 0 0 \n", + "273 49.213800 -2.135800 0 0 0 0 0 \n", + "274 16.742498 -62.187366 0 0 0 0 0 \n", + "275 -24.376800 -128.324200 0 0 0 0 0 \n", + "276 -7.946700 -14.355900 0 0 0 0 0 \n", + "277 21.694000 -71.797900 0 0 0 0 0 \n", + "278 55.378100 -3.436000 0 0 0 0 0 \n", + "279 -32.522800 -55.765800 0 0 0 0 0 \n", + "280 41.377491 64.585262 0 0 0 0 0 \n", + "281 -15.376700 166.959200 0 0 0 0 0 \n", + "282 6.423800 -66.589700 0 0 0 0 0 \n", + "283 14.058324 108.277199 0 2 2 2 2 \n", + "284 31.952200 35.233200 0 0 0 0 0 \n", + "285 39.904200 116.407400 0 0 0 0 0 \n", + "286 15.552727 48.516388 0 0 0 0 0 \n", + "287 -13.133897 27.849332 0 0 0 0 0 \n", + "288 -19.015438 29.154857 0 0 0 0 0 \n", + "\n", + " 1/27/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 \\\n", + "0 0 ... 209322 209340 209358 209362 \n", + "1 0 ... 334391 334408 334408 334427 \n", + "2 0 ... 271441 271448 271463 271469 \n", + "3 0 ... 47866 47875 47875 47875 \n", + "4 0 ... 105255 105277 105277 105277 \n", + "5 0 ... 11 11 11 11 \n", + "6 0 ... 9106 9106 9106 9106 \n", + "7 0 ... 10044125 10044125 10044125 10044125 \n", + "8 0 ... 446819 446819 446819 446819 \n", + "9 0 ... 232018 232018 232619 232619 \n", + "10 4 ... 3900969 3900969 3908129 3908129 \n", + "11 0 ... 104931 104931 105021 105021 \n", + "12 0 ... 1796633 1796633 1800236 1800236 \n", + "13 0 ... 880207 880207 881911 881911 \n", + "14 0 ... 286264 286264 286264 286897 \n", + "15 1 ... 2874262 2874262 2877260 2877260 \n", + "16 0 ... 1291077 1291077 1293461 1293461 \n", + "17 0 ... 5911294 5919616 5926148 5931247 \n", + "18 0 ... 828548 828588 828628 828648 \n", + "19 0 ... 37491 37491 37491 37491 \n", + "20 0 ... 707480 707828 708061 708532 \n", + "21 0 ... 2037773 2037829 2037829 2037829 \n", + "22 0 ... 106645 106645 106645 106645 \n", + "23 0 ... 994037 994037 994037 994037 \n", + "24 0 ... 4717655 4717655 4727795 4727795 \n", + "25 0 ... 70757 70757 70757 70757 \n", + "26 0 ... 27990 27990 27990 27990 \n", + "27 0 ... 62615 62620 62620 62620 \n", + "28 0 ... 1193009 1193256 1193418 1193650 \n", + "29 0 ... 401575 401636 401636 401636 \n", + ".. ... ... ... ... ... ... \n", + "259 0 ... 2805 2805 2805 2805 \n", + "260 5 ... 103443455 103533872 103589757 103648690 \n", + "261 0 ... 170504 170504 170504 170504 \n", + "262 0 ... 5693846 5701249 5701333 5701474 \n", + "263 0 ... 1051998 1052122 1052247 1052382 \n", + "264 0 ... 3904 3904 3904 3904 \n", + "265 0 ... 18799 18814 18814 18814 \n", + "266 0 ... 7305 7305 7305 7305 \n", + "267 0 ... 31472 31472 31472 31472 \n", + "268 0 ... 0 0 0 0 \n", + "269 0 ... 1930 1930 1930 1930 \n", + "270 0 ... 20423 20423 20423 20433 \n", + "271 0 ... 34867 34929 34929 34929 \n", + "272 0 ... 38008 38008 38008 38008 \n", + "273 0 ... 66391 66391 66391 66391 \n", + "274 0 ... 1403 1403 1403 1403 \n", + "275 0 ... 4 4 4 4 \n", + "276 0 ... 2166 2166 2166 2166 \n", + "277 0 ... 6551 6551 6551 6551 \n", + "278 0 ... 24370150 24370150 24396530 24396530 \n", + "279 0 ... 1034303 1034303 1034303 1034303 \n", + "280 0 ... 250932 251071 251071 251071 \n", + "281 0 ... 12014 12014 12014 12014 \n", + "282 0 ... 551981 551986 551986 552014 \n", + "283 2 ... 11526917 11526926 11526937 11526950 \n", + "284 0 ... 703228 703228 703228 703228 \n", + "285 0 ... 535 535 535 535 \n", + "286 0 ... 11945 11945 11945 11945 \n", + "287 0 ... 343012 343012 343079 343079 \n", + "288 0 ... 263921 264127 264127 264127 \n", + "\n", + " 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "0 209369 209390 209406 209436 209451 209451 \n", + "1 334427 334427 334427 334427 334443 334457 \n", + "2 271469 271477 271477 271490 271494 271496 \n", + "3 47875 47875 47875 47875 47890 47890 \n", + "4 105277 105277 105277 105277 105288 105288 \n", + "5 11 11 11 11 11 11 \n", + "6 9106 9106 9106 9106 9106 9106 \n", + "7 10044125 10044125 10044957 10044957 10044957 10044957 \n", + "8 446819 446819 446819 446819 447308 447308 \n", + "9 232619 232619 232619 232619 232619 232974 \n", + "10 3908129 3908129 3908129 3908129 3908129 3915992 \n", + "11 105021 105021 105021 105021 105021 105111 \n", + "12 1800236 1800236 1800236 1800236 1800236 1800236 \n", + "13 881911 881911 881911 881911 881911 883620 \n", + "14 286897 286897 286897 286897 286897 287507 \n", + "15 2877260 2877260 2877260 2877260 2877260 2880559 \n", + "16 1293461 1293461 1293461 1293461 1293461 1293461 \n", + "17 5936666 5940935 5943417 5949418 5955860 5961143 \n", + "18 828682 828721 828730 828783 828819 828825 \n", + "19 37491 37491 37491 37491 37491 37491 \n", + "20 708768 709230 709230 709858 710306 710693 \n", + "21 2037829 2037829 2037829 2037829 2037871 2037871 \n", + "22 106645 106645 106645 106645 106645 106798 \n", + "23 994037 994037 994037 994037 994037 994037 \n", + "24 4727795 4727795 4727795 4727795 4727795 4739365 \n", + "25 70757 70757 70757 70757 70757 70757 \n", + "26 27990 27990 27990 27999 27999 27999 \n", + "27 62620 62620 62620 62620 62627 62627 \n", + "28 1193815 1193908 1193970 1194069 1194187 1194277 \n", + "29 401636 401636 401636 401636 401729 401729 \n", + ".. ... ... ... ... ... ... \n", + "259 2805 2805 2805 2805 2805 2805 \n", + "260 103650837 103646975 103655539 103690910 103755771 103802702 \n", + "261 170504 170504 170504 170504 170544 170544 \n", + "262 5701602 5701743 5701855 5701959 5711818 5711929 \n", + "263 1052519 1052664 1052664 1052926 1053068 1053213 \n", + "264 3904 3904 3904 3904 3904 3904 \n", + "265 18814 18814 18814 18814 18828 18828 \n", + "266 7305 7305 7305 7305 7305 7305 \n", + "267 31472 31472 31472 31472 31472 31472 \n", + "268 0 0 0 0 0 0 \n", + "269 1930 1930 1930 1930 1930 1930 \n", + "270 20433 20433 20433 20433 20433 20433 \n", + "271 34929 34929 34929 34929 34991 34991 \n", + "272 38008 38008 38008 38008 38008 38008 \n", + "273 66391 66391 66391 66391 66391 66391 \n", + "274 1403 1403 1403 1403 1403 1403 \n", + "275 4 4 4 4 4 4 \n", + "276 2166 2166 2166 2166 2166 2166 \n", + "277 6551 6551 6551 6557 6557 6561 \n", + "278 24396530 24396530 24396530 24396530 24396530 24425309 \n", + "279 1034303 1034303 1034303 1034303 1034303 1034303 \n", + "280 251071 251071 251071 251071 251247 251247 \n", + "281 12014 12014 12014 12014 12014 12014 \n", + "282 552051 552051 552125 552157 552157 552162 \n", + "283 11526962 11526966 11526966 11526986 11526994 11526994 \n", + "284 703228 703228 703228 703228 703228 703228 \n", + "285 535 535 535 535 535 535 \n", + "286 11945 11945 11945 11945 11945 11945 \n", + "287 343079 343135 343135 343135 343135 343135 \n", + "288 264127 264127 264127 264127 264276 264276 \n", + "\n", + "[289 rows x 1147 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = pd.read_csv(data_file)\n", "raw_data" @@ -60,11 +1835,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 Afghanistan\n", + "1 Albania\n", + "2 Algeria\n", + "3 Andorra\n", + "4 Angola\n", + "5 Antarctica\n", + "6 Antigua and Barbuda\n", + "7 Argentina\n", + "8 Armenia\n", + "9 Australia\n", + "10 Australia\n", + "11 Australia\n", + "12 Australia\n", + "13 Australia\n", + "14 Australia\n", + "15 Australia\n", + "16 Australia\n", + "17 Austria\n", + "18 Azerbaijan\n", + "19 Bahamas\n", + "20 Bahrain\n", + "21 Bangladesh\n", + "22 Barbados\n", + "23 Belarus\n", + "24 Belgium\n", + "25 Belize\n", + "26 Benin\n", + "27 Bhutan\n", + "28 Bolivia\n", + "29 Bosnia and Herzegovina\n", + " ... \n", + "259 Tuvalu\n", + "260 US\n", + "261 Uganda\n", + "262 Ukraine\n", + "263 United Arab Emirates\n", + "264 United Kingdom\n", + "265 United Kingdom\n", + "266 United Kingdom\n", + "267 United Kingdom\n", + "268 United Kingdom\n", + "269 United Kingdom\n", + "270 United Kingdom\n", + "271 United Kingdom\n", + "272 United Kingdom\n", + "273 United Kingdom\n", + "274 United Kingdom\n", + "275 United Kingdom\n", + "276 United Kingdom\n", + "277 United Kingdom\n", + "278 United Kingdom\n", + "279 Uruguay\n", + "280 Uzbekistan\n", + "281 Vanuatu\n", + "282 Venezuela\n", + "283 Vietnam\n", + "284 West Bank and Gaza\n", + "285 Winter Olympics 2022\n", + "286 Yemen\n", + "287 Zambia\n", + "288 Zimbabwe\n", + "Name: Country/Region, Length: 289, dtype: object" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data['Country/Region']" ] @@ -78,11 +1925,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "24 NaN Belgium 50.8333 4.469936 0 0 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "24 0 0 0 0 ... 4717655 4717655 4727795 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "24 4727795 4727795 4727795 4727795 4727795 4727795 4739365 \n", + "\n", + "[1 rows x 1147 columns]\n" + ] + } + ], "source": [ "belgium_raw = raw_data[raw_data['Country/Region'].str.contains('Belgium')]\n", "print(belgium_raw)" @@ -97,9 +1961,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/201/31/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
240000000000...4717655471765547277954727795472779547277954727795472779547277954739365
\n", + "

1 rows × 1143 columns

\n", + "
" + ], + "text/plain": [ + " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 \\\n", + "24 0 0 0 0 0 0 0 0 \n", + "\n", + " 1/30/20 1/31/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 3/4/23 \\\n", + "24 0 0 ... 4717655 4717655 4727795 4727795 4727795 \n", + "\n", + " 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "24 4727795 4727795 4727795 4727795 4739365 \n", + "\n", + "[1 rows x 1143 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "belgium_cases = belgium_raw.iloc[:, 4:]\n", "belgium_cases" @@ -114,9 +2070,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = plt.gca()\n", "ax.get_xaxis().set_visible(False)\n", @@ -132,11 +2111,127 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", + "59 Anhui China 31.8257 117.2264 1 9 \n", + "60 Beijing China 40.1824 116.4142 14 22 \n", + "61 Chongqing China 30.0572 107.8740 6 9 \n", + "62 Fujian China 26.0789 117.9874 1 5 \n", + "63 Gansu China 35.7518 104.2861 0 2 \n", + "64 Guangdong China 23.3417 113.4244 26 32 \n", + "65 Guangxi China 23.8298 108.7881 2 5 \n", + "66 Guizhou China 26.8154 106.8748 1 3 \n", + "67 Hainan China 19.1959 109.7453 4 5 \n", + "68 Hebei China 39.5490 116.1306 1 1 \n", + "69 Heilongjiang China 47.8620 127.7615 0 2 \n", + "70 Henan China 37.8957 114.9042 5 5 \n", + "71 Hong Kong China 22.3000 114.2000 0 2 \n", + "72 Hubei China 30.9756 112.2707 444 444 \n", + "73 Hunan China 27.6104 111.7088 4 9 \n", + "74 Inner Mongolia China 44.0935 113.9448 0 0 \n", + "75 Jiangsu China 32.9711 119.4550 1 5 \n", + "76 Jiangxi China 27.6140 115.7221 2 7 \n", + "77 Jilin China 43.6661 126.1923 0 1 \n", + "78 Liaoning China 41.2956 122.6085 2 3 \n", + "79 Macau China 22.1667 113.5500 1 2 \n", + "80 Ningxia China 37.2692 106.1655 1 1 \n", + "81 Qinghai China 35.7452 95.9956 0 0 \n", + "82 Shaanxi China 35.1917 108.8701 0 3 \n", + "83 Shandong China 36.3427 118.1498 2 6 \n", + "84 Shanghai China 31.2020 121.4491 9 16 \n", + "85 Shanxi China 37.5777 112.2922 1 1 \n", + "86 Sichuan China 30.6171 102.7103 5 8 \n", + "87 Tianjin China 39.3054 117.3230 4 4 \n", + "88 Tibet China 31.6927 88.0924 0 0 \n", + "89 Unknown China NaN NaN 0 0 \n", + "90 Xinjiang China 41.1129 85.2401 0 2 \n", + "91 Yunnan China 24.9740 101.4870 1 2 \n", + "92 Zhejiang China 29.1832 120.0934 10 27 \n", + "\n", + " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "59 15 39 60 70 ... 2275 2275 2275 \n", + "60 36 41 68 80 ... 40774 40774 40774 \n", + "61 27 57 75 110 ... 14715 14715 14715 \n", + "62 10 18 35 59 ... 17122 17122 17122 \n", + "63 2 4 7 14 ... 1742 1742 1742 \n", + "64 53 78 111 151 ... 103248 103248 103248 \n", + "65 23 23 36 46 ... 13371 13371 13371 \n", + "66 3 4 5 7 ... 2534 2534 2534 \n", + "67 8 19 22 33 ... 10483 10483 10483 \n", + "68 2 8 13 18 ... 3292 3292 3292 \n", + "69 4 9 15 21 ... 6603 6603 6603 \n", + "70 9 32 83 128 ... 9948 9948 9948 \n", + "71 2 5 8 8 ... 2876106 2876106 2876106 \n", + "72 549 761 1058 1423 ... 72131 72131 72131 \n", + "73 24 43 69 100 ... 7437 7437 7437 \n", + "74 1 7 7 11 ... 8847 8847 8847 \n", + "75 9 18 33 47 ... 5075 5075 5075 \n", + "76 18 18 36 72 ... 3423 3423 3423 \n", + "77 3 4 4 6 ... 40764 40764 40764 \n", + "78 4 17 21 27 ... 3547 3547 3547 \n", + "79 2 2 5 6 ... 3514 3514 3514 \n", + "80 2 3 4 7 ... 1276 1276 1276 \n", + "81 0 1 1 6 ... 782 782 782 \n", + "82 5 15 22 35 ... 7326 7326 7326 \n", + "83 15 27 46 75 ... 5880 5880 5880 \n", + "84 20 33 40 53 ... 67040 67040 67040 \n", + "85 1 6 9 13 ... 7167 7167 7167 \n", + "86 15 28 44 69 ... 14567 14567 14567 \n", + "87 8 10 14 23 ... 4392 4392 4392 \n", + "88 0 0 0 0 ... 1647 1647 1647 \n", + "89 0 0 0 0 ... 1521816 1521816 1521816 \n", + "90 2 3 4 5 ... 3089 3089 3089 \n", + "91 5 11 16 26 ... 9743 9743 9743 \n", + "92 43 62 104 128 ... 11848 11848 11848 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "59 2275 2275 2275 2275 2275 2275 2275 \n", + "60 40774 40774 40774 40774 40774 40774 40774 \n", + "61 14715 14715 14715 14715 14715 14715 14715 \n", + "62 17122 17122 17122 17122 17122 17122 17122 \n", + "63 1742 1742 1742 1742 1742 1742 1742 \n", + "64 103248 103248 103248 103248 103248 103248 103248 \n", + "65 13371 13371 13371 13371 13371 13371 13371 \n", + "66 2534 2534 2534 2534 2534 2534 2534 \n", + "67 10483 10483 10483 10483 10483 10483 10483 \n", + "68 3292 3292 3292 3292 3292 3292 3292 \n", + "69 6603 6603 6603 6603 6603 6603 6603 \n", + "70 9948 9948 9948 9948 9948 9948 9948 \n", + "71 2876106 2876106 2876106 2876106 2876106 2876106 2876106 \n", + "72 72131 72131 72131 72131 72131 72131 72131 \n", + "73 7437 7437 7437 7437 7437 7437 7437 \n", + "74 8847 8847 8847 8847 8847 8847 8847 \n", + "75 5075 5075 5075 5075 5075 5075 5075 \n", + "76 3423 3423 3423 3423 3423 3423 3423 \n", + "77 40764 40764 40764 40764 40764 40764 40764 \n", + "78 3547 3547 3547 3547 3547 3547 3547 \n", + "79 3514 3514 3514 3514 3514 3514 3514 \n", + "80 1276 1276 1276 1276 1276 1276 1276 \n", + "81 782 782 782 782 782 782 782 \n", + "82 7326 7326 7326 7326 7326 7326 7326 \n", + "83 5880 5880 5880 5880 5880 5880 5880 \n", + "84 67040 67040 67040 67040 67040 67040 67040 \n", + "85 7167 7167 7167 7167 7167 7167 7167 \n", + "86 14567 14567 14567 14567 14567 14567 14567 \n", + "87 4392 4392 4392 4392 4392 4392 4392 \n", + "88 1647 1647 1647 1647 1647 1647 1647 \n", + "89 1521816 1521816 1521816 1521816 1521816 1521816 1521816 \n", + "90 3089 3089 3089 3089 3089 3089 3089 \n", + "91 9743 9743 9743 9743 9743 9743 9743 \n", + "92 11848 11848 11848 11848 11848 11848 11848 \n", + "\n", + "[34 rows x 1147 columns]\n" + ] + } + ], "source": [ "china_raw = raw_data[raw_data['Country/Region'].str.contains('China')]\n", "print(china_raw)" @@ -151,11 +2246,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/201/31/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
05486439201406207528775509608781419802...4903524490352449035244903524490352449035244903524490352449035244903524
\n", + "

1 rows × 1143 columns

\n", + "
" + ], + "text/plain": [ + " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 \\\n", + "0 548 643 920 1406 2075 2877 5509 6087 \n", + "\n", + " 1/30/20 1/31/20 ... 2/28/23 3/1/23 3/2/23 3/3/23 3/4/23 \\\n", + "0 8141 9802 ... 4903524 4903524 4903524 4903524 4903524 \n", + "\n", + " 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "0 4903524 4903524 4903524 4903524 4903524 \n", + "\n", + "[1 rows x 1143 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "china_cases = china_raw.iloc[:, 4:].sum(axis=0).to_frame().T\n", "china_cases" @@ -170,9 +2357,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAADuCAYAAAAeC/GzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHvFJREFUeJzt3Xl4XNWd5vHvryQh75skL3gHG4wNhMSKbQLZTGKcpRs6A4m7k8Y97cSZPOl08nRmJqEn8zAhpAf6yYRJ5hmYOISOIR2MO0tDOiHGbUhYAgYZA8YLsbCNLduy9sWWJUuq3/xRp6BUCEm2SrqqqvfzPHrq1rnnnHskkvP63nPrlrk7IiIigxWLegAiIpIbFCgiIpIRChQREckIBYqIiGSEAkVERDJCgSIiIhmhQBERkYxQoIiISEYoUEREJCMKox7AcCotLfV58+ZFPQwRkayyY8eOOncv669eXgXKvHnzqKioiHoYIiJZxcxeH0g9XfISEZGMGFCgmNkhM9tlZi+aWUUom2JmW81sf3idnFL/ZjOrNLNXzezalPKloZ9KM/u+mVkoLzazB0P5djObl9JmbTjGfjNbm1I+P9TdH9qeN/g/h4iInKuzOUP5oLtf4e7l4f3XgW3uvhDYFt5jZouBNcASYDVwl5kVhDZ3A+uBheFndShfBzS6+wLgTuCO0NcU4BZgObAMuCUluO4A7gzHbwx9iIhIRAZzyes6YGPY3ghcn1K+yd073P0gUAksM7MZwAR3f8YTz8y/L61Nsq+fAdeEs5drga3u3uDujcBWYHXYtzLUTT++iIhEYKCB4sCjZrbDzNaHsmnufhwgvE4N5TOBIyltq0LZzLCdXt6jjbt3Ac1ASR99lQBNoW56Xz2Y2XozqzCzitra2gH+uiIicrYGepfXVe5+zMymAlvNbF8fda2XMu+j/Fza9NVXz0L3DcAGgPLycn2bmIjIEBnQGYq7HwuvNcAvSaxnnAiXsQivNaF6FTA7pfks4Fgon9VLeY82ZlYITAQa+uirDpgU6qb3JSKS89ydeHzgP8Px7bz9nqGY2Vgg5u6tYXsVcCvwMLAWuD28PhSaPAz81My+C5xPYvH9OXfvNrNWM1sBbAduAv5PSpu1wDPADcBj7u5mtgX4h5SF+FXAzWHf46HuprTji4jkrObTnWx67jCbK47wWu2pAbf79797PwumjhvCkQ3sktc04JfhDt9C4Kfu/lszex7YbGbrgMPAjQDuvtvMNgN7gC7gi+7eHfr6AvBjYDTwSPgB+BFwv5lVkjgzWRP6ajCzbwHPh3q3untD2P4asMnMbgN2hj5ERHJSZ3ecDU8c4Ae/f42W9i6Wzp3Ml1bOoDA2sKXwKWOH/pMVNhynQSNFeXm565PyIpJt4nHn8z/ZwdY9J7hm0VT+btVFLDl/4rAd38x2pHxk5G3l1aNXRESy0YYnD7B1zwm+8bFL+Ox7L4h6OG9Lj14RERnBalrb+c6WV/noZdNZd/X8qIfTJwWKiMgItnXPCbrizpevuYiwlj1iKVBEREawLbtPMLdkDBdNG9o7tDJBgSIiMkJ1dsfZfqCeD148dcSfnYACRURkxNpzrIWOrjjvnjcl6qEMiAJFRGSE2vF6IwBL507up+bIoEARERmhdhxuZOak0UyfOCrqoQyIAkVEZIR64fXGrDk7AQWKiMiIdLTpNMeb2xUoIiIyONm2fgIKFBGREemF1xsZXVTAounjox7KgClQRERGoB2vN3LF7EkUFmTPNJ09IxURyRNtZ7rYc7yF8nnZc7kLFCgiIiPOi4eb6I4775qjQBERkUF4srKOwpjx7vnZ8Qn5JAWKiMgI8+T+Wt41dzLjirPrK6sUKCIiI0j9yQ5eOdrC+xaWRj2Us6ZAEREZQZ6qrAPgvQvLIh7J2VOgiIiMIE/ur2PSmCIunTl83xmfKQoUEZERwt15cn8tVy0opSA28r//JJ0CRURkhNhfc5ITLR28Pwsvd4ECRURkxHhkVzUAV2fhgjwoUERERoynKmt555xJnD9pdNRDOScKFBGREaD5dCevHG3h8ixcjE9SoIiIjAA/3X6Y053d3Fg+O+qhnDMFiohIxDq6urn36YO8d2FpVt4unKRAERGJ2La9NdS2dvDZ914Q9VAGRYEiIhKxbXtrmDi6iKsuLIl6KIOiQBERiZC781RlLVcvLM2qL9PqTXaPXkQkyx1pOM2Jlg5WZNmj6nsz4EAxswIz22lm/xbeTzGzrWa2P7xOTql7s5lVmtmrZnZtSvlSM9sV9n3fzCyUF5vZg6F8u5nNS2mzNhxjv5mtTSmfH+ruD23PG9yfQkRk+D1/qAEg6777pDdnc4byZWBvyvuvA9vcfSGwLbzHzBYDa4AlwGrgLjMrCG3uBtYDC8PP6lC+Dmh09wXAncAdoa8pwC3AcmAZcEtKcN0B3BmO3xj6EBHJKjsONzJhVCEXTR0f9VAGbUCBYmazgI8B96QUXwdsDNsbgetTyje5e4e7HwQqgWVmNgOY4O7PuLsD96W1Sfb1M+CacPZyLbDV3RvcvRHYCqwO+1aGuunHFxHJGnuPt3DJjAnEsvBhkOkGeobyv4H/CsRTyqa5+3GA8Do1lM8EjqTUqwplM8N2enmPNu7eBTQDJX30VQI0hbrpffVgZuvNrMLMKmprawf464qIDL143PljdSuLpmf/2QkMIFDM7ONAjbvvGGCfvcWs91F+Lm366qtnofsGdy939/Kysux8gqeI5KajTac5daabi6dPiHooGTGQM5SrgD81s0PAJmClmf0EOBEuYxFea0L9KiD12QGzgGOhfFYv5T3amFkhMBFo6KOvOmBSqJvel4hIVthX3QrAxflyhuLuN7v7LHefR2Kx/TF3/wzwMJC862ot8FDYfhhYE+7cmk9i8f25cFms1cxWhDWQm9LaJPu6IRzDgS3AKjObHBbjVwFbwr7HQ93044uIZIVXq1uA3AmUwv6rvK3bgc1mtg44DNwI4O67zWwzsAfoAr7o7t2hzReAHwOjgUfCD8CPgPvNrJLEmcma0FeDmX0LeD7Uu9XdG8L214BNZnYbsDP0ISKSNfZVtzJr8mjGFQ9mKh45LPGP/fxQXl7uFRUVUQ9DRASAD3/398wtGcM9a98d9VD6ZGY73L28v3r6pLyISASONLSxv+Yk75o7uf/KWUKBIiISgUP1pwAon5v9n5BPUqCIiESgtrUDgLLxxRGPJHMUKCIiEUgGSum43HkMoQJFRCQCrze0MXlMEeNHFUU9lIxRoIiIRKDiUAMLpo6LehgZpUARERlmbWe6+OOJk7xvYW49DkqBIiIyzOpPngFg2oRREY8ksxQoIiLDrOFUIlCmjM2dBXlQoIiIDLvjze2AzlBERGSQDtYlPtQ4r3RMxCPJLAWKiMgwO1h3krLxxTl1yzAoUEREht2B2lPMLx0b9TAyToEiIjLMDtad4gIFioiIDEZzWyf1p87oDEVERAbnYHjKsAJFREQG5enKOgAWTZ8Q8UgyT4EiIjKMdh9r5oLSscwpya1bhkGBIiIyrKqb25k+Mbc+0JikQBERGSbNbZ28cqyFuTl4dgIKFBGRYfNiVRNnuuJ8/PLzox7KkFCgiIgMkz9WtwKweEbuLciDAkVEZNgcqDvJlLHnMTnHnjKcpEARERkmr9Xm5ifkkxQoIiLD5EDtyZz8QGOSAkVEZBgcaWij7uQZFp+fm+snoEARERkWv3r5GAAfvHhqxCMZOgoUEZFhcKD2FNMnjGKeLnmJiMhgvHK0mQVTx0U9jCGlQBERGWKH69vYV93K+y4qjXooQ0qBIiIyxO5/9hCFMeMjl86IeihDqt9AMbNRZvacmb1kZrvN7JuhfIqZbTWz/eF1ckqbm82s0sxeNbNrU8qXmtmusO/7ZmahvNjMHgzl281sXkqbteEY+81sbUr5/FB3f2ibm58UEpGs5u48VVnPO+dMYvaU3HyGV9JAzlA6gJXu/g7gCmC1ma0Avg5sc/eFwLbwHjNbDKwBlgCrgbvMrCD0dTewHlgYflaH8nVAo7svAO4E7gh9TQFuAZYDy4BbUoLrDuDOcPzG0IeIyIjy6J4T7D3ewp9eMTPqoQy5fgPFE06Gt0Xhx4HrgI2hfCNwfdi+Dtjk7h3ufhCoBJaZ2Qxggrs/4+4O3JfWJtnXz4BrwtnLtcBWd29w90ZgK4lAM2BlqJt+fBGRESEed3701EHKxhfz5++eHfVwhtyA1lDMrMDMXgRqSEzw24Fp7n4cILwmb66eCRxJaV4VymaG7fTyHm3cvQtoBkr66KsEaAp10/tKH/t6M6sws4ra2tqB/LoiIhlx79MHee5gA1/50EIKC3J/yXpAv6G7d7v7FcAsEmcbl/ZR3Xrroo/yc2nTV189C903uHu5u5eXlZX1VkVEJONeONzIP255lQ9eXMZfLJsT9XCGxVlFprs3Ab8jsfZxIlzGIrzWhGpVQOq53SzgWCif1Ut5jzZmVghMBBr66KsOmBTqpvclIhKpl6uaWHvvc0yfMIrvfvIKwv1HOW8gd3mVmdmksD0a+BCwD3gYSN51tRZ4KGw/DKwJd27NJ7H4/ly4LNZqZivCGshNaW2Sfd0APBbWWbYAq8xscliMXwVsCfseD3XTjy8iEplXjjbzmXu2M3F0EQ+sX5Gzj6rvTWH/VZgBbAx3asWAze7+b2b2DLDZzNYBh4EbAdx9t5ltBvYAXcAX3b079PUF4MfAaOCR8APwI+B+M6skcWayJvTVYGbfAp4P9W5194aw/TVgk5ndBuwMfYiIRGb3sWY+fc92xo8q4oHPrWDmpNFRD2lYWeIf+/mhvLzcKyoqoh6GiOSgvcdb+IsfPsvoogIe/PyVOfWZEzPb4e7l/dXL/dsORESG2KvVrXz6nu2MKirggfUrcipMzoYCRURkECprTvLpe56lqMD46edWMLckd58m3B8FiojIOXrlaDNrNjwLJMIkl7+NcSAUKCIiZyked374xAH+7K6nKYwZm9Yv58Ky3H40/UAM5C4vEREJmk938qUHdvLEH2u5dsk0bv/E5Xl1a3BfFCgiIgNU3dzO2nuf40DdSW67/lI+vXxO3nxocSAUKCIiA9DUdoYbf/AHGk918uP/uIyrFuT2l2WdCwWKiEg/4nHnq5tforq5nQc/fyXvmjO5/0Z5SIvyIiL9+METB9i2r4ZvfGyxwqQPChQRkT68Wt3Kdx59lY9dNoObrpwb9XBGNAWKiMjbcHe++avdjCsu5LbrL9UCfD8UKCIib2PrnhP84bV6vrrqIt0aPAAKFBGRXnR2x7n9kX1cWDY2b74ga7AUKCIivfjnZ1/nQN0p/v6jl+TF1/dmgv5KIiJpmk938r1t+3nPhSWsXDQ16uFkDQWKiEia72/bT9PpTv7bxy7RQvxZUKCIiKR48UgTG/9wiE+Vz2bJ+ROjHk5WUaCIiAQt7Z186YEXmDZhFDd/5JKoh5N19OgVERESnzn5z5tf4lhTO5s/fyUTxxRFPaSsozMUERFgwxMHeHTPCW7+yCKWztXjVc6FAkVE8t4zr9Vzx2/38dHLprPu6vlRDydrKVBEJK/teL2Bv/npC8wrHcs/3vAO3dU1CAoUEclL7s4/PX2QT/3gWcaNKuSHN5UzrljLyoOhv56I5J1THV3c/ItdPPzSMT50yTT+1yffwcTRWoQfLAWKiOSVA7Un+U8/2UFlzUn+y7UX84X3X0gspstcmaBAEZG88euXj/O1n7/MeYUx7vvr5Vy9UF/jm0kKFBHJeTWt7dz+m338YudR3jF7End9+l3MnDQ66mHlHAWKiOSs02e6uefJA9z9+9fo7I7zpZUL+NtrFlKkpwcPCQWKiOQUd2fv8VZ+ubOKf9lRRVNbJ6uXTOfrH1nEvNKxUQ8vpylQRCQruTtNbZ0caWyjqvE0VY1t7Ktu5Q+V9VS3tFMYMz68eBqffe98ls6dEvVw84ICRURGvNrWDrYfrGff8Vb2VbdypKGNqsY2Tp3p7lFvytjzuPKCEt67sJRVS6YzRV/bO6wUKCIyYr10pInbH9nHswfrcYeCmHFB6VjmlozlygtLmD1lDLMmjw4/Y/RZkoj1GyhmNhu4D5gOxIEN7v49M5sCPAjMAw4Bn3T3xtDmZmAd0A38rbtvCeVLgR8Do4HfAF92dzez4nCMpUA98Cl3PxTarAW+EYZzm7tvDOXzgU3AFOAF4C/d/cwg/hYiMoL809MH+fav91Iy7jy+fM1CVi6aysXTx1NcWBD10ORtDORWhy7gq+5+CbAC+KKZLQa+Dmxz94XAtvCesG8NsARYDdxlZsn/BdwNrAcWhp/VoXwd0OjuC4A7gTtCX1OAW4DlwDLgFjNLPgb0DuDOcPzG0IeI5ID7n32db/5qDx9cNJVHv/J+vvKhi7h81iSFyQjXb6C4+3F3fyFstwJ7gZnAdcDGUG0jcH3Yvg7Y5O4d7n4QqASWmdkMYIK7P+PuTuKMJLVNsq+fAddY4glt1wJb3b0hnP1sBVaHfStD3fTji0gWO1zfxj/8ei/vv6iM//eZpfpekixyVjdjm9k84J3AdmCaux+HROgAU0O1mcCRlGZVoWxm2E4v79HG3buAZqCkj75KgKZQN72v9DGvN7MKM6uora09m19XRCLwnUdfJWbwPz9xGQV6JEpWGXCgmNk44OfAV9y9pa+qvZR5H+Xn0qavvnoWum9w93J3Ly8rK+utioiMEHUnO/jNruOsWTaH8/VJ9qwzoEAxsyISYfLP7v6LUHwiXMYivNaE8ipgdkrzWcCxUD6rl/IebcysEJgINPTRVx0wKdRN70tEstSW3dV0xZ0by2f1X1lGnH4DJaxX/AjY6+7fTdn1MLA2bK8FHkopX2NmxeFOrIXAc+GyWKuZrQh93pTWJtnXDcBjYZ1lC7DKzCaHxfhVwJaw7/FQN/34IpKlfvtKNfNLx3LxtPFRD0XOwUA+h3IV8JfALjN7MZT9PXA7sNnM1gGHgRsB3H23mW0G9pC4Q+yL7p789NEXePO24UfCDyQC634zqyRxZrIm9NVgZt8Cng/1bnX3hrD9NWCTmd0G7Ax9iEiWajx1hj+8Vs/n33eBvjUxS/UbKO7+FL2vWQBc8zZtvg18u5fyCuDSXsrbCYHUy757gXt7KT9A4lZiEckBW/eeoDvufOTSGVEPRc6RHrkpIiPCI7uOM2vyaC6dOSHqocg5UqCISOROn+nm6cp6rl0yXZe7spgCRUQit/NII2e641y1oCTqocggKFBEJHLbDzQQMyifp8fMZzMFiohEbvvBehafP4EJo/SYlWymQBGRSHV0dbPzcBPL5+tyV7ZToIhIpF460kxHV5zl83W5K9spUEQkUtsP1APwbq2fZD0FiohE6rlDDSyaPp7J+rrerKdAEZHIdHbH2fF6oy535QgFiohEZtfRZtrOdLP8Ai3I5wIFiohE5tmwfrJMZyg5QYEiIpF55rV6Fk0fT+m44qiHIhmgQBGRSHR0dfP8oQauvFCXu3KFAkVEIrHzcBPtnXHec2Fp1EORDFGgiEgktu45QVGBaf0khyhQRGTYdXbH+dedR/nQJdOYOFrP78oVChQRGXa/faWa+lNnuLF8VtRDkQxSoIjIsHJ3NjxxgAtKx/KBi6ZGPRzJIAWKiAyrZ16rZ9fRZj73vguIxfTtjLlEgSIiw+oHTxygdFwxf/bOmVEPRTJMgSIiw6ay5iS//2Mtf/WeuYwqKoh6OJJhChQRGTb/UnGEgpjxqXfPiXooMgQUKCIyLDq74/z8haOsXDSVsvF61EouUqCIyLB4fF8NdSc7+FT57KiHIkNEgSIiw+LnL1RRNr6YD1xcFvVQZIgoUERkyLV3dvPEH+v4yKXTKSzQtJOr9F9WRIbc05V1nO7s5kOXTIt6KDKEFCgiMuT+fe8JxhUXsvwCPQgylylQRGTIPfNaPSsuKKG4UJ89yWUKFBEZUs1tnRyqb+OdcyZFPRQZYv0Gipnda2Y1ZvZKStkUM9tqZvvD6+SUfTebWaWZvWpm16aULzWzXWHf983MQnmxmT0Yyreb2byUNmvDMfab2dqU8vmh7v7Q9rzB/ylEZCjsOtoMwOWzJkY8EhlqAzlD+TGwOq3s68A2d18IbAvvMbPFwBpgSWhzl5klz3HvBtYDC8NPss91QKO7LwDuBO4IfU0BbgGWA8uAW1KC6w7gznD8xtCHiIxAL1U1AXD5TJ2h5Lp+A8XdnwAa0oqvAzaG7Y3A9Snlm9y9w90PApXAMjObAUxw92fc3YH70tok+/oZcE04e7kW2OruDe7eCGwFVod9K0Pd9OOLyAjzclUT80rGMHGMvkgr153rGso0dz8OEF6TX2owEziSUq8qlM0M2+nlPdq4exfQDJT00VcJ0BTqpvf1Fma23swqzKyitrb2LH9NERmsl6uauXyWzk7yQaYX5Xv7cgPvo/xc2vTV11t3uG9w93J3Ly8r0yd0RYZTTWs7x5vbtX6SJ841UE6Ey1iE15pQXgWkPqhnFnAslM/qpbxHGzMrBCaSuMT2dn3VAZNC3fS+RGQEeflIYkH+HbN1hpIPzjVQHgaSd12tBR5KKV8T7tyaT2Lx/blwWazVzFaENZCb0tok+7oBeCyss2wBVpnZ5LAYvwrYEvY9HuqmH19ERpCXq5qIGSw5f0LUQ5FhUNhfBTN7APgAUGpmVSTuvLod2Gxm64DDwI0A7r7bzDYDe4Au4Ivu3h26+gKJO8ZGA4+EH4AfAfebWSWJM5M1oa8GM/sW8Hyod6u7J28O+BqwycxuA3aGPkRkhNl9rIUFU8cx5rx+pxrJAZb4B39+KC8v94qKiqiHIZI3rrr9MZbOncz3//ydUQ9FBsHMdrh7eX/19El5ERkSLe2dHG06zaIZ46MeigwTBYqIDIlXq1sBuGS61k/yhQJFRIbEvuMtAFw8XWco+UKBIiJDYm91KxNGFTJj4qiohyLDRIEiIkPicH0b88vGEZ4DK3lAgSIiQ6LuZAdTxxdHPQwZRgoUERkSdSc7KB2nQMknChQRybjuuNNw6gxl4/RVRflEgSIiGVd/qoO4Q6kueeUVBYqIZFxd6xkAynTJK68oUEQk4+pOdgA6Q8k3ChQRybg3AkVnKHlFgSIiGfdmoGhRPp8oUEQk4060dDC6qIBxxXpsfT5RoIhIxlW3tDN94ih9Sj7PKFBEJONONLczbYLWT/KNAkVEMq66pZ3pE/RQyHyjQBGRjHJ3alo6mKanDOcdBYqIZFRjWydnuuNMG69AyTcKFBHJqOrmdgCm6wwl7yhQRCSjTrQkAmWa1lDyjgJFRDKqukVnKPlKgSIiGZW85KUv18o/ChQRyaia1nZKx51HUYGml3yj/+IiklHVze1aP8lTChQRyZj2zm72VbcyY+LoqIciEVCgiEhGtHd28/n7d3C8uZ2/es+8qIcjEdCjQEVkUNydww1tfONfX+Gpyjpu/8RlXL2wNOphSQQUKCIRcHfcIe5OPLy++d6Jx1O2HbrjTmd3nO640xX3Xt93dcff3E57P7i2Tne85/vO7jit7Z20tHfRcOoMDafOUBAz7vgPl/PJ8tlR/3klIgqUPJScUFInjuSEljqpveU9iYkwOQF2dSfKu+PJV1K2nW534vG0/Sllb2z3KIN4sv4b5W+OIdnOwyTbHXc81O2O99I2Of43xkGPMb+xnRxbjzH1MtGnTfqeXpZyvPTASA+PkaYwZhQWGIWxGAUxo6jAKIi9+T6xzyiIxSgqMCaMKmL6xFFMHF3E4hkT+MDFU5k9ZUzUv4ZESIEySDUt7bx4pInm0520nenmZEcXpzq6aDvTzZnueGLC7vaw7XTFkxN4cjJ6czLrOTn13J86saVOqMnJK+5v9vHGxJk6uYb33SN0MhsIMygwIxYzYinbBTEjZomfglii3CxRXhCzN9q9US+W0tbeLCuKxVL6SRwjlrLfkvtCeeI9afvfPH4suT+MIdE2UZa630KdWEp/ljxOzDCgsCBM6jGjsCAWJvbkpD/w96mBkRogyTGJDEZWB4qZrQa+BxQA97j77cN5/FseeoWNz7z+lvKCmDHmvAKKC2MUxmIUFhjnFcTe+D9vYUHPySMxgSQmjeR2+uSSPnEVpExWb0xUMXpMiKkT8BvbZhSFsRSljSf1uEbK+1jP929MjmFCLoilTM4x3lKWnMgTE/ybbd8ssx79xWKkhYRpwhPJAlkbKGZWAPxf4MNAFfC8mT3s7nuG8rjxuPOrl49x9+9eY191Kx+7bAZ/ffV8ysYVM7a4gLHFhRQXxjT5iUjeydpAAZYBle5+AMDMNgHXARkPlIpDDRyqb6OqsY0tu0+w93gLF00bx3//+GI+s2IOxYUFmT6kiEjWyeZAmQkcSXlfBSxPr2Rm64H1AHPmzDmnA931u9d4bF8NZnDp+RP57iffwfVXzCQW01mIiEhSNgdKb7P5W5ab3X0DsAGgvLz8nJaj/8efLOGWP1lMybhixhVn859MRGToZPPsWAWk3vA+Czg2FAeaU6JbIUVE+pPNj155HlhoZvPN7DxgDfBwxGMSEclbWXuG4u5dZvY3wBYStw3f6+67Ix6WiEjeytpAAXD33wC/iXocIiKS3Ze8RERkBFGgiIhIRihQREQkIxQoIiKSEebZ+ujZc2BmtcBbn+YoIiJ9mevuZf1VyqtAERGRoaNLXiIikhEKFBERyQgFioiIZIQCRUREMkKBIiIiGaFAERGRjFCgiIhIRihQREQkIxQoIiKSEf8fsuk+BZ6VwloAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ax = plt.gca()\n", "ax.get_xaxis().set_visible(False)\n", @@ -188,7 +2398,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "scrolled": true }, @@ -197,10 +2407,7 @@ "france_raw = raw_data[raw_data['Country/Region'].str.contains('France')]\n", "# print(france_raw)\n", "france_cases = france_raw.iloc[:, 4:].sum(axis=0).to_frame().T\n", - "# france_cases\n", - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "# plt.plot(france_cases.T)" + "# france_cases" ] }, { @@ -212,17 +2419,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "germany_raw = raw_data[raw_data['Country/Region'].str.contains('Germany')]\n", "# print(germany_raw)\n", "germany_cases = germany_raw.iloc[:, 4:]\n", - "# germany_cases\n", - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "# plt.plot(germany_cases.T)" + "# germany_cases" ] }, { @@ -234,16 +2438,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "italy_raw = raw_data[raw_data['Country/Region'].str.contains('Italy')]\n", "italy_cases = italy_raw.iloc[:, 4:]\n", - "# italy_cases\n", - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "# plt.plot(italy_cases.T)" + "# italy_cases" ] }, { @@ -255,34 +2456,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "japan_raw = raw_data[raw_data['Country/Region'].str.contains('Japan')]\n", "japan_cases = japan_raw.iloc[:, 4:]\n", - "# japan_cases\n", - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "# plt.plot(japan_cases.T)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "\n", - "# plt.plot(belgium_cases.T, label='Belgium')\n", - "# plt.plot(china_cases.T, label='China')\n", - "# plt.plot(france_cases.T, label='France')\n", - "\n", - "# # Add labels and legend\n", - "# plt.ylabel('Cumulative cases')\n", - "# plt.legend()" + "# japan_cases" ] }, { @@ -294,16 +2474,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "netherlands_raw = raw_data[raw_data['Country/Region'].str.contains('Netherlands')]\n", "netherlands_cases = netherlands_raw.iloc[:, 4:].sum(axis=0).to_frame().T\n", - "# netherlands_cases\n", - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "# plt.plot(netherlands_cases.T)" + "# netherlands_cases" ] }, { @@ -315,16 +2492,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "spain_raw = raw_data[raw_data['Country/Region'].str.contains('Spain')]\n", "spain_cases = spain_raw.iloc[:, 4:]\n", - "# spain_cases\n", - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "# plt.plot(spain_cases.T)" + "# spain_cases" ] }, { @@ -336,33 +2510,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "UK_raw = raw_data[raw_data['Country/Region'].str.contains('United Kingdom')]\n", "UK_cases = UK_raw.iloc[:, 4:].sum(axis=0).to_frame().T\n", - "# UK_cases\n", - "# ax = plt.gca()\n", - "# ax.get_xaxis().set_visible(False)\n", - "# plt.plot(UK_cases.T)" + "# UK_cases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization of different countries the cumulative number of people with SARS-CoV-2 disease since the beginning of the pandemic" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'netherlands_cases' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitaly_cases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Italy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjapan_cases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Japan'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetherlands_cases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Netherlands'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspain_cases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Spain'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mUK_cases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'United Kingdom'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'netherlands_cases' is not defined" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD2CAYAAAA54puTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3XlYVdX6wPHvOnCYZQZRFEFFcUKQwTFDc6iuY2qTaeattMHb5DWtLOtX3WbLsixzajArTVOzJE1yCHPOVFRQEUVAUebxDOv3x0FCmQ7CYdD1eR4eOfusvfd7RHn33mutdwkpJYqiKIoCoGnoABRFUZTGQyUFRVEUpZRKCoqiKEoplRQURVGUUiopKIqiKKVUUlAURVFKNcmkIIRYLIQ4L4Q4ZEbbuUKIAyVfx4UQmfURo6IoSlMkmuI8BSFEfyAX+EJK2bUG+00DQqWUky0WnKIoShPWJO8UpJRbgUtltwkh2gkhfhFC7BVCbBNCBFWw6z3AN/USpKIoShNk3dAB1KHPgKlSynghRE/gY2Dg5TeFEG2AAOC3BopPURSl0bsukoIQwgnoA3wvhLi82faqZncDK6WUhvqMTVEUpSm5LpICpsdgmVLKkCra3A08Vk/xKIqiNElNsk/halLKbOCUEGIcgDDpfvl9IURHwA2IbaAQFUVRmoQmmRSEEN9g+gXfUQhxVgjxb2A88G8hxF/AYWBkmV3uAVbIpjjUSlEUpR41ySGpiqIoimU0yTsFRVEUxTJUUlAURVFKNbnRR56entLf37+hw1AURWlS9u7dmy6l9KquXZNLCv7+/uzZs6ehw1AURWlShBCnzWmnHh8piqIopVRSUBRFUUqppKAoiqKUsnifghDCCtgDJEsph131ngA+AG4H8oFJUsp9NT2HTqfj7NmzFBYW1kXISgOys7OjVatWaLXahg5FUW5I9dHR/AQQBzhX8N5tQGDJV0/gk5I/a+Ts2bM0a9YMf39/yhTEU5oYKSUXL17k7NmzBAQENHQ4inJDsujjIyFEK+BfwOeVNBmJaaEcKaXcCbgKIVrU9DyFhYV4eHiohNDECSHw8PBQd3yK0oAs3afwPjADMFbyvi9wpszrsyXbakwlhOuD+jkqSsWM0kiRocji57HY4yMhxDDgvJRyrxAiqrJmFWwrV4xJCPEw8DCAn59fncVYl6ysrOjWrRtSSqysrPjoo4/o06dPlfs4OTmRm5tbZZsHH3yQp59+ms6dO9dluIqiNKACfQEZhRlkFGVwqeASlwovkVOcQ05xDtnF2WQXZ5e+zinOIas4i/SCdB7s9iCPhVh2BQBL9in0BUYIIW4H7ABnIcRXUsr7yrQ5C7Qu87oVcO7qA0kpP8O0shrh4eGNsoKfvb09Bw4cAGDjxo3MmjWL33//vdbH/fzzyp68KYrS2Ekpic+MZ/PpzcRnxnMy8yTn8s5RoC+odB9HrSPNbJrhbONMM5tmtHBqQUebjnjaexLWPMziMVssKUgpZwGzAEruFKZflRAA1gKPCyFWYOpgzpJSplgqpvqSnZ2Nm5tb6eu3336b7777jqKiIkaPHs3LL798RXuj0cjjjz/O77//TkBAAEajkcmTJzN27FiioqJ45513CA8Pv+LOYuXKlaxfv56lS5cyadIk7O3tOXr0KKdPn2bJkiUsW7aM2NhYevbsydKlS+vz4yvKDc8ojexI3sGCgws4eOEgAoGfsx9tXdrSx7cPHnYeuNu542rripudGx52HjjbOuOodcRa07CFJur97EKIqQBSygXABkzDURMwDUl9oLbHf3ndYY6cy67tYa7QuaUzLw3vUmWbgoICQkJCKCwsJCUlhd9+My0FHR0dTXx8PLt27UJKyYgRI9i6dSv9+/cv3feHH34gMTGRv//+m/Pnz9OpUycmT55coxgzMjL47bffWLt2LcOHD2fHjh18/vnnREREcODAAUJCqlqUTlGUuiKlZPaO2aw9sZZmNs2YGTmTAa0H0NKpZUOHZpZ6SQpSyhggpuT7BWW2S66TJTLLPj6KjY1l4sSJHDp0iOjoaKKjowkNDQUgNzeX+Pj4K5LC9u3bGTduHBqNBh8fHwYMGFDj8w8fPhwhBN26daN58+Z069YNgC5dupCYmKiSgqLUg5ziHJ6JeYbYlFgmdp7Iw8EP42Lr0tBh1UiTK4hXnequ6OtD7969SU9P58KFC0gpmTVrFlOmTKm0vbkLHZUdmXP1sE1bW1sANBpN6feXX+v1+pqEryjKNZq1bRaxKbGM6zCOZ8KfQSOaXtGIphdxE3D06FEMBgMeHh4MHTqUxYsXl/YFJCcnc/78+Sva9+vXj1WrVmE0GklLSyMmJqbC4zZv3py4uDiMRiOrV6+29MdQFMVMBqOB57Y9x+9nf+eZsGd4sfeLTTIhwHV4p9BQLvcpgOnKf9myZVhZWTFkyBDi4uLo3bs3YBqG+tVXX+Ht7V2675gxY9i8eTNdu3alQ4cO9OzZExeX8recb7zxBsOGDaN169Z07dq12uGsiqLUjyWHl7Du5DoifCK4O+hui5xDSoksLkZT5kmAJTS5NZrDw8Pl1espxMXF0alTpwaKqG7k5ubi5OTExYsXiYyMZMeOHfj4+DR0WA3ievh5KjeOlcdX8nLsywxsPZAPBn5QZ8c1ZGeTF7uTnOhoik6cQJeUhPsDD+A17fFrOp4QYq+UMry6dupOoZEYNmwYmZmZFBcXM3v27Bs2IShKU7I5aTMvx76Mr5Mv0yOm1+pYxvx8Cg8fJn/ffnK3bqXwyBFkQQFWLi7YhXTHISIC+x6hdRR55VRSaCQq60dQFKVx+vbot7z656sEuQex/F/L0WrMr+xrLCig+MwZCg8epPDYcfJ37qQoMRF0OgDsunXDZdRInG+9DftuXdE4OFjoU5SnkoKiKEoNZBRm8NnBz/gq7ivau7bn1b6vVpsQdCkp5O/ZS25MDEXHj1N08iQYDAAIW1scwnrgFHUzDuHh2HbogLZFjeuC1hmVFBRFUcyQVZTFljNbeHfPu2QWZTK4zWDe7v82VhqrSvcpjIvj0tKlZG34GXQ6rNzdsQ8OxumWgdi2D8SucydsAgIaVSFIlRQURVGqcKnwEl8d+YrlR5eTp8vD39mfuVFz6dG8R6XDTnXJyVyYN4+cXzchdTpc/vUv3MaPx7ZDoMVHD9WWSgqKoigViLsYx7z98/gz5U90Rh2h3qFM7jqZ8ObhONk4VbiPsbiYjC++4OKSpRiys3EID6PFK69g07p1he0bI5UU6lBqaipPPvkku3fvxtbWFn9/f0aNGsXatWtZv359ufaqLLaiND5GaeTLI1/y/r73cdQ6MsR/CJO7TibQNbDSxzxSSnJ/+430Tz+j8OBBbNq1o83SJdgGBtZz9LWnkkIdkVIyevRo7r//flasWAHAgQMHWLduXaX7qLLYitL4vL/vfZYcWkLrZq1ZdusyvBy8qmyfu20bGd+sIPe33xD29vjOfQ/n226rp2jrXtOch90IbdmyBa1Wy9SpU0u3hYSEcNNNN5Gbm8vYsWMJCgpi/PjxpbWOoqKiuDwRz8nJieeff57u3bvTq1cv0tLSAFi3bh09e/YkNDSUQYMGlW5XFKVuGaWRt3a/xZJDSxgTOIafRv9UaUKQUpK9MZqkKVM489DD5G7diteTT9Ah9o8mnRDgerxT+HkmpP5dt8f06Qa3vVFlk0OHDhEWVvECGPv37+fw4cO0bNmSvn37smPHDvr163dFm7y8PHr16sVrr73GjBkzWLhwIS+88AL9+vVj586dCCH4/PPPeeutt3j33Xfr7KMpimKy+NBivjzyJRE+ETwV9lSFj4qkwUDGNyvIXLWKorg4NE5OeP5nGh4PPIDG3r4Boq57119SaIQiIyNp1aoVYLp7SExMLJcUbGxsGDZsGABhYWH8+uuvAJw9e5a77rqLlJQUiouLCQgIqN/gFeUGsOjvRXyw7wMG+Q3ivaj3yiUEKSUZXy/n4qefor9wAdvA9jR//nnc7r0HYVX5kNSm6PpLCtVc0VtKly5dWLlyZYXvlS1lbWVlVWEpa61WW/oPsWybadOm8fTTTzNixAhiYmKYM2dO3QevKDewX079wvv73qe9a3umR0wvlxB058+TMnMWeX/8gV33YLyffRbnf93eqOYW1CWL9SkIIeyEELuEEH8JIQ4LIV6uoE2UECJLCHGg5OtFS8VjaQMHDqSoqIiFCxeWbtu9e3et12nOysrC19cXgGXLltXqWIqi/ENKyXt73+O/W/9LV4+ufD/8e3ydfP9532Ag45tvSBw7jvw9e/B64j/4r1iBy7B/XbcJASx7p1AEDJRS5gohtMB2IcTPUsqdV7XbJqUcZsE46oUQgtWrV/Pkk0/yxhtvYGdnVzoktTbmzJnDuHHj8PX1pVevXpw6daqOIlaUG1d6QTrv732fH0/8yM2tbmZm5MxyayOnL1hA+ocfofX1xf+7b7ELCmqgaOtXvZTOFkI4ANuBR6SUf5bZHgVMr0lSuF5LZyv/UD9PxVL0Rj1bzmzh1Z2vcqnwEsPaDuP1fq+Xu/LP++MPkqZMpdmAAfi+PxehafoDNRtF6WwhhBWwF2gPzC+bEMroLYT4CziHKUEctmRMiqLceLKLs4k9F8viQ4s5cvEIPo4+fHX7VwR7BpdLCIVxcSRN/jdCq8X7maevi4RQExZNClJKAxAihHAFVgshukopD5Vpsg9oU/KI6XZgDVBuCqAQ4mHgYQA/Pz9LhqwoynUivSCd38/8zqakTexN20uBvgB3O3fm9J7DYP/BONs4V7jfpa+/Bq2Wdht/QduyZT1H3fDqZfSRlDJTCBED3AocKrM9u8z3G4QQHwshPKWU6Vft/xnwGZgeH9VHzIqiND2F+kK+P/4925O388e5PwDwd/bnVv9bGdV+FJ09OmNnbVfp/tkbo8lauQrXcWNvyIQAFkwKQggvQFeSEOyBQcCbV7XxAdKklFIIEYlpNNRFS8WkKMr1R0rJgQsHmL9/PnvP70Vv1ONl78XU7lPp1aIXPbx7mDVayJCZybnpptXT3B94wNJhN1qWvFNoASwr6VfQAN9JKdcLIaYCSCkXAGOBR4QQeqAAuFs2tUWjFUVpMOfzz/NMzDMcuHAAdzt3xgSOYaDfQPq07FPjY2WuXoPU6QhY/QO2bdtaINqmwWJJQUp5ECi3oGhJMrj8/UfAR5aKQVGU69fq+NW8svMVtBotT/R4glHtR+Fp73lNx5JGIxnffIN99+7Y3eAj366/Gc0NxMrKim7dupW+XrNmDf7+/g0XkKJcx3an7ubFP16kk3snno18lrDmFdcdM9eZRx5Bl5SEx+Qb97HRZSop1BF7e3sOHDhQ6ft6vR5ra/XXrSi1tT15O49segR3O3eW3roUB23tFrXXpaWRt2079t2743LHHXUUZdN1Yw3ArWdLly5l3LhxDB8+nCFDhpCbm8stt9xCjx496NatGz/++CMAiYmJdOrUiYceeoguXbowZMgQCgoKAEhISGDQoEF0796dHj16cOLECQDefvttIiIiCA4O5qWXXmqwz6go9Uln1PHqzlfxtvfmgwEf1DohFJ08xbkZz4LRSMs330BjY1NHkdYNKSWZaakci93Gjm+/5OT+3RY/53V36frmrjc5eulonR4zyD2IZyOfrbJNQUEBISEhAAQEBLB69WoAYmNjOXjwIO7u7uj1elavXo2zszPp6en06tWLESNGABAfH88333zDwoULufPOO1m1ahX33Xcf48ePZ+bMmYwePZrCwkKMRiPR0dHEx8eza9cupJSMGDGCrVu30r9//zr93IrS2Gw/u53k3GQ+HPghId4htTqWsaiIM1OmoDtzBtdxY7FpJI97C3JzOB67naRDf3E27hD5WZkACI2GnqPvpG1ohEXPf90lhYZS2eOjwYMH4+7uDpiy/nPPPcfWrVvRaDQkJyeXLpoTEBBQmlTCwsJITEwkJyeH5ORkRo8eDYCdnWl8dXR0NNHR0YSGmvrxc3NziY+PV0lBua4l5yaz4dQGACJ9Imt1LF1yMmlvvInuzBn8lizGsXfvugixVrLTz/PH98s5tmMrel0xTh6e+AeH0rJjJ3zadcCjlR/W9XAnc90lhequ6Oubo6Nj6fdff/01Fy5cYO/evWi1Wvz9/SksLATKl9cuKCigstG5UkpmzZrFlClTLBu8ojQiM7bO4OCFg7R1aXvNj42klOTv3s25Gc+iT03F7d57GjwhSCk5uOlntn69FKPRQOebB9J98O14tQlokGqs111SaMyysrLw9vZGq9WyZcsWTp8+XWV7Z2dnWrVqxZo1axg1ahRFRUUYDAaGDh3K7NmzGT9+PE5OTiQnJ6PVavH29q6nT6Io9etiwUUOXjjIXR3v4umwp6/pGPr0dNJef53sDT9j5eZGwOofGnz4aVF+PluWfsrh3zfTukswQx6ehqtPiwaNSSWFejR+/HiGDx9OeHg4ISEhBJlRivfLL79kypQpvPjii2i1Wr7//nuGDBlCXFwcvUuucJycnPjqq69UUlCuW6/EvgLAsLbDanyXYMzL4+KSpaTPnw+Ax0MP4jZhAtoG/v9SlJ/Hytdmk3oinl533EWfceMbRfG9eimdXZdU6ezrn/p5KmUlZCQweu1oXGxd2HLnFrQarVn7SYOBzB9+IO3V15BFRTQbPBj3yQ/gEFpuTm29k0Yj6+e9TcKuPxj+1CzaR/Sy+DkbRelsRVGU2tpwagMaoWHNyDVmJ4SCw4c5++hj6NPScAgPx/3Bf+N0882NZsW03et+4HjsNnqPvbdeEkJNqKSgKEqjtjlpM5E+kWaVsJDFxaTMfpGsH3/E2scHn5dfxnXsGISVVT1Eap7s9Avs+PYrAnv2offYexo6nHJUUlAUpdE6n3+ek1knGd5ueLVtC48dI+1/b5C/cyfOt9+G98yZDd5vUJG961cjpZGoCQ82mjuXslRSUBSlUTJKI3euuxOA8OZVPwovPHac0+Pvw5ibi/fMZ/GYNKkeIqy5i8ln2PfLOrpGDcLZq/ElLFBJQVGURmpnyk4uFl5kVPtRdPfqXmm7i4sWc/7tt7H29iZgzWpsWrWqxyhr5sDG9Vhba7np3kkNHUqlGn78k6IoSgXWJKzBxdaF2b1mV/qYpfDoUc6/9x723bvjt2Rxo04I0mgkflcsAT3CcXB2aehwKqWSQh1KS0vj3nvvpW3btoSFhdG7d+/SGkiKopgvOjGan0/9zK3+t2JjVXFph+zoaE6NGo2ViwutP12Abbt29RxlzaSdTCAv4xLtwno2dChVslhSEELYCSF2CSH+EkIcFkK8XEEbIYSYJ4RIEEIcFEL0sFQ8lialZNSoUfTv35+TJ0+yd+9eVqxYwdmzZ83a32AwWDhCRWkaTmaeZOa2mdhb23N3x7srbCN1OtJe/x9Wnp74LV6ElatrPUdZc3t+WoOVVkvbHpYtaFdblrxTKAIGSim7AyHArUKIqwfk3gYElnw9DHxiwXgs6rfffsPGxoapU6eWbmvTpg3Tpk3DYDDw3//+t7TU9aeffgpATEwMAwYM4N5776Vbt24kJiYSFBTEgw8+SNeuXRk/fjybNm2ib9++BAYGsmvXLgB27dpFnz59CA0NpU+fPhw7dgwwleq+4447uPXWWwkMDGTGjBkALFq0iKeeeqo0roULF/L009dWKkBRLOlk5kke/+1x7Kzt+PmOn2nv1r5cGykl6Qs+RZ+aSotXXsHOjMoADa0wL5djsdsIvXU49s2cGzqcKllyOU4J5Ja81JZ8XT19eiTwRUnbnUIIVyFECyllyrWeN/X11ymKq9vS2badgvB57rkq2xw+fJgePSq+0Vm0aBEuLi7s3r2boqIi+vbty5AhQwDTL/hDhw4REBBAYmIiCQkJfP/993z22WdERESwfPlytm/fztq1a3n99ddZs2YNQUFBbN26FWtrazZt2sRzzz3HqlWrADhw4AD79+/H1taWjh07Mm3aNO6++26Cg4N566230Gq1LFmypDQxKUpjcSLzBBM2TCBHl8MrfV7Bw96jwnY5P/9M+vz5aNv44dT/pnqO8tqcOx4HUhIQUu2E4gZn0dFHQggrYC/QHpgvpfzzqia+wJkyr8+WbLvmpNBYPPbYY2zfvh0bGxvatGnDwYMHWblyJWAqjBcfH4+NjQ2RkZEEBASU7hcQEFC6rGeXLl245ZZbEEKU3klc3v/+++8nPj4eIQQ6na50/1tuuQUXF1MnVufOnTl9+jStW7dm4MCBrF+/nk6dOqHT6a5YOlRRGlJ6QToLDy5k+dHl2Ghs2HDHBlo3a11p+0vLvsDKxYWAlSsRTWQ1w7Nxh9FYWdMisENDh1Iti/6NSikNQIgQwhVYLYToKqU8VKZJRUMKyhVjEkI8jOnxEn5+flWes7orekvp0qVL6dU6wPz580lPTyc8PBw/Pz8+/PBDhg4desU+MTExV5TWhitLaGs0mtLXGo0GvV4PwOzZsxkwYACrV68mMTGRqKioCve3srIq3efBBx/k9ddfJygoiAceUOvQKg2rUF/I1rNbWXdyHdvObsMgDUT4RHCL3y1VJoSihAQK/voL72efxapZs3qMuHaS4w7TvG07tLZ2DR1Ktepl9JGUMhOIAW696q2zQNl/Aa2AcxXs/5mUMlxKGe7l5WWxOGtj4MCBFBYW8skn/3SL5OfnAzB06FA++eST0iv648ePk5eXd83nysrKwtfXFzD1I5ijZ8+enDlzhuXLl3PPPY1var1yYziZeZIJGyYQ8XUEz/z+DIfSDzGxy0TWjFzD4qGLGd9pfKX76tLOc3LYcNBocBlR/QznxiI/O4vUE/H4BnVp6FDMYrE7BSGEF6CTUmYKIeyBQcCbVzVbCzwuhFgB9ASyatOf0JCEEKxZs4annnqKt956Cy8vLxwdHXnzzTcZN24ciYmJ9OjRAyklXl5erFmz5prPNWPGDO6//37ee+89Bg4caPZ+d955JwcOHMDNze2az60o1yKjMIO5e+eWzj0YEziGof5DifCJwFpj3q+hjBXfAND8heex9qi4v6Ex2vrVYkDSdcDghg7FLBYrnS2ECAaWAVaY7ki+k1K+IoSYCiClXCBMM1I+wnQHkQ88IKXcU9kxQZXOro1hw4bx1FNPccsttzR0KFVSP8/ry8bEjbz+5+tkF2czNnAsDwU/hLdDzUo8SKORhEGDsG3bDr/PF1oo0rqXduoEX818goiRY+nfwLOYG7x0tpTyIFCucLmUckGZ7yXwmKViUEwyMzOJjIyke/fujT4hKNePQn0hc/fOZfnR5XRy78SHAz8k2Cu4xseRRiNnpkxFfy4F76efsUCklnMsdhsaKysiR45t6FDM1jS67pVacXV15fjx4w0dhnID0Rl0/Oe3/xCbEsvo9qOZ3Xu22WshXC1vxw7ytm3DsU9vnG8dWv0OjUjigb34duyMnaNTQ4diNpUUFEWpU1JK5sTOITYlllf6vMLowNG1Ol7G18ux8vSk9YIFTWYIKkDupYtcOH2qURe/q0i1o4+EEE8IIZxLSlIsEkLsE0IMqY/gFEVpelYcW8HaE2u5J+ieWieE9IULyY2Jwe3OOxE2FddAaqyOxW4HICAkrIEjqRlzhqROllJmA0MAL+AB4A2LRqUoSpNUoC9g/oH59GzRk1mRs2p1LF3aeS7MfR+h1eJ2b9MbRv33bxtp0b4jnn7+DR1KjZiTFC5PMLsdWCKl/IuKJ50pinIDMxgNPLjxQbKKsnio20O1XlUsc+X3YDTS9qf1WHtWvxRnY5KZlsrFs0l07NO/Ua6uVhVzksJeIUQ0pqSwUQjRDDBaNqymx8nJ1JGUmJjI8uXLq22fmJhI165dLR2WotSbmDMxHEw/yPTw6fRsUbvy0Dm/bSH9409w7NcPm2qqGDRGJ/eZile2DWvcFVErYk5S+DcwE4iQUuYDNpgeISkVMDcpKMr15uujX9PCsUWVs5LNYczPJ2XWLDAY8HriP3UUXf06uW837i1b4ebTsqFDqTFzkoIEOgOXfzqOQOMv4NFAZs6cybZt2wgJCWHu3LkkJiZy00030aNHD3r06MEff/xRbp+bbrqJAwcOlL7u27cvBw8erM+wFaVW0vLS2J26mzsC7zB7hnJlMr//HkNWFm2+WY59EyzcWFyQz5nDf9M2LLKhQ7km5vz0Psb0uGgg8AqQA6wCGuV90bbvjpN+Jrf6hjXg2dqJm+40r7rhG2+8wTvvvMP69esBU/2jX3/9FTs7O+Lj47nnnnu4ekb2gw8+yNKlS3n//fc5fvw4RUVFBAfXfJKPojSU+QfmAzCkTe0GJl74aD7pH32EQ2QkDqHl5r42CX//Fo3RoKddE00K5twp9JRSPgYUAkgpMzA9QlLMoNPpeOihh+jWrRvjxo3jyJEj5dqMGzeO9evXo9PpWLx4MZMmTar/QBXlGqUXpLP2xFpGtBtBW9e213ycvD93kf7RR9h1D6b58w1T7bi2pJT89esGfIO6NJkCeFcz505BV7IugoTSQneNtqPZ3Cv6+jJ37lyaN2/OX3/9hdFoxM6u/JM3BwcHBg8ezI8//sh3331X7k5CURqzjYkbMUgDk7tOvuZjZG/YQPLTz6D19aXNsmVoKvh/0hRcPHOajJRzhP1rVJMbdXSZOUlhHrAa8BZCvAaMBV6waFRNWLNmzcjJySl9nZWVRatWrdBoNCxbtqzStZgffPBBhg8fzk033YS7u3t9hasotbbh1AaC3INo59quxvvqL10i87vvSP9sIVpfX1p9PL/JJgSA+N2xIATtwq9eebjpqDYpSCm/FkLsBW7BND9hlJQyzuKRNVHBwcFYW1vTvXt3Jk2axKOPPsqYMWP4/vvvGTBgQLlFdS4LCwvD2dlZLYCjNCmpeakcvHCQx0JqVtdSSknOxmjOv/suujNn0LZsid+yZdi08rVQpPUjYddOWgYG4eTWdC/sqk0KQoh2wCkp5XwhRBQwWAiRUrJwjlIiN9fUua3Vatm8efMV75UdSfS///0PAH9/fw4d+mcRunPnzmE0GkvXblaUpuD9fe8jENziZ3713aJTp0if/zHZ69ejcXTE74tlOERENNnHLZelJhznfOIJ+t937Y9ab1pjAAAgAElEQVTRGgNzOppXAQYhRHvgcyAAUAPx69AXX3xBz549ee2119Bo6mUxPEWptYzCDDae2sjdQXcT6BZo1j6527Zz8l/DyN6wAc/HH6dD7B84RkY2+YQAsHvtKuycmtFtQNO+sDOnT8EopdQLIe4APpBSfiiE2G/pwG4kEydOZOLEiQ0dhqLUSHRiNHqp547AO8xqn7lyJamvvoaNvz++c9/DrmNHC0dYf/IyM0jYs5PQW4dj51T7MtnSKJFSkn2xEIPOSG5GEQDOnna4+VT8CLqumDv66B5gInB5YdRqC6MLIVoDXwA+mEYrfSal/OCqNlHAj8Cpkk0/SClfMS90RVEa0q9JvxLgEkBHt+p/uef9uYuUF2Zj5e5O608XYNO6dbX7NCWHtvyK0WAgeNDVy9BfSUpJQY6O3IxCMtPyycssJj+nmLyMQooK9ORmFKErMpCXUYTRWH5VzB5D/eg9ur2lPgZgXlJ4AJgKvCalPCWECAC+MmM/PfCMlHJfSb2kvUKIX6WUVw/U3yalHFazsBVFaUiXCi+xO3U3EzpNqPbRT3FSEuemT0fr60vbn9Y36dFFFSkuyOevX3+mdeduuLdsVe59KSVpidkciE7iwtlcsi8UXPG+xkqgtbPCxdMe+2Y2eLW2xdbBGjsnLfbNbLCxt8LRxRYrrQZHF1uLfx5zRh8d4Z8SF0gpT2FG6WwpZQqQUvJ9jhAiDvAFys/eUhSlSVl4cCFGaeTWgKqvjI15eSTeOx5DejqtFnxy3SUEgD/XfE/OxQsMnfpE6TajUZKwJ43kYxnExaYiS676PXyd6HazL56tm+HZ2gk7Jy3N3O0aVZ+KOaOPAoH/Yap/VPoTlVKaPXVRCOGPab3mPyt4u7cQ4i/gHDBdSnm4gv0fBh4G8GuCFRMV5XpSoC/gxxM/cpv/bXT1rLrS76UvvihNCM2iouonwHpUlJ/P/l/W07FPf9oEh2AwGIn56ijJxzLJuVQIgJVWQ+htbWjdyZ2W7V0bOOLqmfP4aAnwEjAXGIDpcZLZaU0I4YRpBNOTJYv1lLUPaCOlzBVC3A6sAcoNY5BSfgZ8BhAeHl7+QVsj4eTkVDo0VVGuV78l/UZOcQ7jOo6rst3FJUu58ME8nAbdcl0mBIDDv29CV1hA2L9G8sunf5N46CIGnRFbR2v8gz25+Z4O2DlpsdZamXU8g6EQgyGfoqI0jMYCiorOo9fnotNdoqDwDF6eg/Hw6G/Rz2ROUrCXUm4WQggp5WlgjhBiG6ZEUSUhhBZTQvhaSvnD1e+XTRJSyg1CiI+FEJ5SyvQafAZFUerRL4m/4O3gTVjzypeZNOTmkT5/PtZeXvg8/3w9Rld/CnJz2P3jSpy9/IldnUvKiSx82rrg1dqJPmPaozMkI2UKxTq4mBFHTs5hdMWXKCpOw2jUIY069IY89PpsjMYijMYi9Pqrr5vL0mBr69MokkKhEEIDxAshHgeSAe/qdhKmh2SLgDgp5XuVtPEB0qSUUggRiWnexEWzo2+EcnNzGTlyJBkZGeh0Ol599VVGjhxJYmIit956Kz179mT//v106NCBL774AgcHB1555RXWrVtHQUEBffr04dNPP0UIQVRUFD179mTLli1kZmayaNEibrrppob+iMoNbG/aXmLOxDCh8wQ0ovI5Ndnr12HMzcXv84VoW7SoxwjrnpRGiopSMRgKMRoL0RvyKCo8x/5fN2LX8gT2nu2xclpAYIds3FpoKNIlse2PLIzGgnLHsrJywME+AI2VLUJosbVtjpNTRzQaGzQaW2y07lhZOWJj44mVlR22ti3QWNlhZ9sCjUaLRtMIOpqBJwEHTJ3N/4fpEdL9ZuzXF5gA/C2EuLxYwHOAH4CUcgGmOkqPCCH0QAFwt5SyVo+Htiz9jPOnT9bmEOV4t2nLgEkPm9XWzs6O1atX4+zsTHp6Or169WLEiBEAHDt2jEWLFtG3b18mT57Mxx9/zPTp03n88cd58cUXAZgwYQLr169n+HDT6F+9Xs+uXbvYsGEDL7/8Mps2barTz6Yo5jIYDby681V8HH2YEjyl0nb6jAwufDAPm7ZtsQ8JqccIr51en4dOd5GMjF0UF58nK/svdLpM8vISMBrzMRqLy+1j4wutfEHKNKytXLGxdUZYeeBsH4yNjSf29m2wtfECBEJjjadHFFZWDvX/4WrInNFHu0u+zaUGK65JKbdTTd+DlPIj4CNzj9kUSCl57rnn2Lp1KxqNhuTkZNLS0gBo3bo1ffv2BeC+++5j3rx5TJ8+nS1btvDWW2+Rn5/PpUuX6NKlS2lSuOMO08SgsLAwEhMTG+QzKQqYqqEmZCbwdv+3cbF1qbTdhQ8+wJCRgduE++oxusoZDIVIqaOgIAm9IZ/CwmT0ukwKC8+RX5BIYeE5cnOPUbb4s5WVI06OHXB374utbXPs7XzB4My5hAJSEwo4dzgOXd4hetz+DL1H34yVleWv4OuLOaOPfgXGXa51JIRwA1ZIKYdaOrhrYe4VvaV8/fXXXLhwgb1796LVavH396ew0DQK4ephZ0IICgsLefTRR9mzZw+tW7dmzpw5pe0BbG1N/9isrKzQ6/X190EUpYyzOWd5Y9cbtHdtzxD/yss4SKOR3M2/4RAejufUqfUSW17eCQoLz5Kdcwi9Ppu8vBMYjUWmX/76bHS6jAr3E8Iaa+tmNHPqTPPmw3B1CcfOriWuruFoNHZoNFqklBTl69kfnUTqySzOxWdi1J+nOOc4gT2j6De2aZe0qIg5j488yxa/k1JmCCGq7VO4UWVlZeHt7Y1Wq2XLli2cPn269L2kpCRiY2Pp3bs333zzDf369StNAJ6enuTm5rJy5UrGjh3bUOErSjnFhmKeinkKvVHP//X9vyr7EopPnkR/4QJeTz6BsFAdr4zM3WRl7ibt/AYKCs5gMPwz4k8Ia2xsvLCza4GDgz+2Ns2xtWuBlcYWW1sfrK2bYWfni5WVA3Z2vohKPos0So7uTGHX2lOlQ0utbTS0D/fm0ulfyKAZQ6ZMs8jna2hm1T4SQvhJKZMAhBBtKFlwR/mHXq/H1taW8ePHM3z4cMLDwwkJCSEoKKi0TadOnVi2bBlTpkwhMDCQRx55BAcHh9KV2fz9/YmIaJSrnCo3KL1Rz5w/5nD00lE+HPhhlfMS9BkZZK5eDYB9jx51HktKyiqSk78hK9tUek2jscXTYyAOjm1xc+2JnV0rHBza1Po8mefz2bTkCGmnsvFu04ygPi1w83EgMLw5f2+J5tCvRxj88OPYOda+xlFjZE5SeB7YLoT4veR1f0omkin/OHz4MO3atcPT05PY2Nhy7ycmJqLRaFiwYEG591599VVeffXVcttjYmJKv/f09FR9Ckq90hv1zN4xm/Un1zOx80SiWkdV2T7lhdnkbt6Mtbc3Nv7+dRKD0agnNXUNZ5O/ID8/ESsrRzw8bibA/3GaNeuGRlNtGTazSSk5sv0cO1YloNEIBk3qRIdIH4TG9Ng3PSmRzZ9/TMsOneg6YHCdnbexMaej+RchRA+gF6aO46fUPIIrLViwgHnz5vH+++83dCiKUid0Bh0zts5gU9ImpoVO4+Hgqq8DjXl55G3bhsvIEXg9/Uytyzbk5Z3gQvomTp/+FL0+CwBPz0H4tX4AN7e6X9VMV2Rg27fHifsjBd8OrkSND8K1+T8jhQx6HRvmv4eNgyMj//sCGo15k9GaInPuFChJAustHEuTNXXqVKZW06l29aI6itJYSSmZEzuHTUmbmBExgwmdJ1S7T96ffyKLi3EZfQfa5jXvciwuTic39xjpF2PIytpHdrZpFLu7W188vQbj7tYbR0fLVAfNuVTIT/P/4mJyHiGDWtPnjvaldwcARqOBn+a9zYXEk4yc/gIOzpWPvLoemJUUFEW5cSw7vIy1J9bySPdHzEoIhtw8Up5/AWFvj32P0ArbSGmkoCCJvLwE01d+PDpdBsVF6ej0WRQWngVM/QSOjoG0azcDT48BODoGWqxYnL7YwMGYs+xefwqhEQyf1h2/Lh5XtDEaDWxaOJ/4P/+g3z330z6i6a69bC6VFBRFKbUndQ/v7n2Xvr59q31kdFnOxo0YMjJwGTkCjY0NYJobkJ6+idy8eDIz/iQ3Lx69/p8VfG1tfbCx8UJr446DYztatrwT52bBODsHo9Va7kpcSklGaj4n9p3n75izFOToaNPNgz53tMe9xZWL1xQXFvDTvLc5uXcXPUffRc9RVdd6ul6YlRSEEP2AQCnlEiGEF+BUUkJbUZTryLt73sXXyZf3bn4Pa41514x5O3Zg5elJizfeoLj4EnFHZ3Hx4lakNM0CdnTsgJfXYFxdeuDo2AFHx3ZYWzez5Me4gtEoST2Zxam/0jn11wWyzpvKT/h1dqfH0Da07OBa7m4kNeE4P388l4xzyQycPJXQoTfOki/mTF57CQgHOmKqmKrFtMhOX8uGpihKffrp5E8cuniIR0MexUFrXjkGaTSSFxuL7eBw9h+YQEaGaeRdy5Z34+19G26uEfVSr6ciF5NzORqbwrE/UynI0aGxEvh2dKP7wNYEdPfEya382g5F+fns+PZLDmz8CUc3N+547mX8gyt+JHa9MudSYDSmtRD2AUgpz5WspKaUIYTg6aef5t133wXgnXfeITc3lzlz5lS6T0xMDDY2NvTp0weASZMmMWzYsFpNXvP392fPnj14enpe8zEuU6XAbxz5unxe+/M12ru2Z3jb4dXvUKL45ElyOqZzJuonNFm2tPF7GGfnELy9G6bggZSSkwcu8NfmM6QkZCEEBHT3on24N226eGBjX/GvPKPRwOm/9rN5yQKyzqcRMuR2+t414bqdi1AVc5JCcUkVUwkghLDsqtFNlK2tLT/88AOzZs0y+xdyTEwMTk5OpUmhNqQ0LfStKNci+nQ0OcU5fHzLx7RqVn5JycokxL5E5kQDzWw707HrK7i4NMxVta7IwJEd5/hr8xlyLhbi7GVPnzHt6dS7BXZOVc9lSD56hM2LPuZCUiJOHp7cPedNfIM611PkjY8589C/E0J8CrgKIR4CNgELLRtW02Ntbc3DDz/M3Llzy7134cIFxowZQ0REBBEREezYsYPExEQWLFjA3LlzCQkJYdu2bQBs3bqVPn360LZtW1auXFl6jLfffpuIiAiCg4N56SXTUhaJiYl06tSJRx99lB49enDmzJkrzjtq1CjCwsLo0qULn332Wel2Jycnnn/+ebp3706vXr1KC/adOnWK3r17ExERwezZs0vbp6Sk0L9/f0JCQujatWtprMr1Y2PiRhy1jnT36m72PslHvyTVdyeaHEGPyG8bLCEc353Ksud2sP27eJzcbBk8uTPjX+5F6GC/KhOCrqiQjQvmseKlGeTnZHP7tOlMfv/TGzohgHmT194RQgwGsjH1K7wopfzV4pFdo8x1Jyg+l1enx7Rp6Yjr8HbVtnvssccIDg5mxowZV2x/4okneOqpp+jXrx9JSUkMHTqUuLg4pk6dipOTE9OnTwdg0aJFpKSksH37do4ePcqIESMYO3Ys0dHRxMfHs2vXLqSUjBgxgq1bt+Ln58exY8dYsmQJH3/8cbl4Fi9ejLu7OwUFBURERDBmzBg8PDzIy8ujV69evPbaa8yYMYOFCxfywgsv8MQTT/DII48wceJE5s+fX3qc5cuXM3ToUJ5//nkMBgP5+fm1/BtVGpNCfSHbk7fT3rW92cM/dbpM4pPfwjpZ0NX7f1ib2QdRl4xGyc41J9gfnYRPWxf6jGlPi3bmjVzKzbjE6jdf5nziSSJGjKHn6LuwdWj8Za3rgzkdzU8B3zfmRNBYODs7M3HiRObNm4e9vX3p9k2bNnHkyJHS19nZ2eTk5FR4jFGjRqHRaOjcuXPpFXx0dDTR0dGEhpquxHJzc4mPj8fPz482bdrQq1fFY6fnzZvH6pJaNGfOnCE+Ph4PDw9sbGwYNsw0miIsLIxffzX9aHfs2MGqVasA07oOzz77LAARERFMnjwZnU7HqFGjCGkiNfIV86w/aZqXOqV75WsklCWlkb8PPY5B5OO50h73VaMsGV6FjEbJpsWHid9znqDePkTdF4SVlXkF+ArzcvnxnVfJOJfMqP/Opl1YpIWjbVrM6VNwBjYKIS4BK4CVUso0y4Z17cy5orekJ598kh49evDAA/8sPWE0GomNjb0iUVTmcqlsoLSPQErJrFmzmDLlyv+0iYmJODpW3MUTExPDpk2biI2NxcHBgaioqNKKrFqttvSK8OqS3BVdKfbv35+tW7fy008/MWHCBP773/8yceLEaj+L0jSsO7GOdi7tGOxnXj2f1LS1ZGTEYpfWDGeHjght3dUfMoeUkq0rjhO/5zy9R7ejx1Dzi+AZ9DpWvTab84mnGP7UTJUQKlBtapVSviyl7AI8BrQEfhdCVLv8lxCitRBiixAiTghxWAjxRAVthBBinhAiQQhxsKTGUpPm7u7OnXfeyaJFi0q3DRkyhI8++mctoQMHTFP4mzVrVukdQ1lDhw5l8eLFpSOBkpOTOX/+fJX7ZGVl4ebmhoODA0ePHmXnzp3Vnqdv376sWLECMK0Lcdnp06fx9vbmoYce4t///jf79u2r9lhK06Az6DiUfoh+vv2wMqOej9Go48SJt2nm1BX3twX2XYPrIcorHY1N5fDWZEKH+NUoIQDErlxB6ol4bp82/YaYnXwtalLw/DyQimkNZXOKm+iBZ6SUnTAV03tMCHF1D85tQGDJ18PAJzWIp9F65plnSE//p2bgvHnz2LNnD8HBwXTu3Lm0Uurw4cNZvXr1FR3NFRkyZAj33nsvvXv3plu3bowdO7baZHLrrbei1+sJDg5m9uzZlT5iKuuDDz5g/vz5REREkJWVVbo9JiaGkJAQQkNDWbVqFU88US6/K03UsYxjFBuLCfYy75d7fMJrFBWl0tJ2OOQXYte1i4UjvFJBTjE7VsbTMtCVXqNq9lQg+Vgcu9Z8T5ebB9Gxdz8LRdj0ieqGMQohHgHuAryAlcC3UsojVe5U8XF+BD4q2zdRMqopRkr5TcnrY0CUlDKlsuOEh4fLPXv2XLEtLi6OTp061TQkpZFSP8/683Xc17yx6w1+HfsrPo4+VbbNyNzNvn13Y2/nR6fC6aQ+Oh3/b1dg3938EUu1IaXkx7n7OZeQxbiZ4Xj5mT9dyqDXsWz6Yxj0Bia+9eEN2akshNgrpQyvrp05fQptgCellAdqEYw/pglwf171li9Qdhzl2ZJtVyQFIcTDlKzh4Ofnd61hKIpylbiLcXjYeVSbEAAST32IrU1zIiN/Ir5rGABaX19Lh1jq1F/pJB/P5OZ7OtQoIQD8/duvZKScY/TMl27IhFATlT4+EkI4l3z7FpAkhHAv+2XuCYQQTsAqTIkl++q3K9il3K2LlPIzKWW4lDLcy8vL3FMrilKN09mnCXAJqLZdfPzrXMrYQcuWd2JMNa157NCzJ9Z1MHPeHFJK9mxIxLW5A537tazRvrriIv78YQUtO3YmIKTaC+UbXlV9CstL/twL7Cn5c2+Z19USQmgxJYSvpZQ/VNDkLNC6zOtWwDlzjq0oSu0l5STRxrnqztqUlB9IOrMIV9dIWrW6j6LjxwHwqse+pTNxl7iQlEPwgFZozBx6etlfG38iN+MSN9090WJluK8nlT4+klIOK/mz+suICgjT3/4iIE5K+V4lzdYCjwshVgA9gayq+hMURak7F/IvcKnwUqVJQUpJUtJCEk68iYtLGD1Cv0IIK7JLkoJth8B6iVMaJX+sOoGzpx2d+rao0b5Gg4E9P63Br1sIrTpXvr608o9qU64QYrM52yrQF5gADBRCHCj5ul0IMVUIcXmZsg3ASSABU+mMR80PXVGU2ticZPpv3Ne3fMFjo7GYU6fmkXDiTZydQ+jYYQ5CmIasFh4+hLaNH1ZO9VMs7vjuNC4m59JrZDustTVbBvP03wfIy7hE98G3WSi660+ldwpCCDvAAfAUQrjxz/N/Z0zzFaokpdxOxX0GZdtITPMfFEWpZ/EZ8TjbOBPoeuUVf05OHEeOPENu3jFcXSNL7xAuKzj4Nw4REfUSY1GBnp1rTuDZ2on2YTVf5vPw75uxc3SibQ81Sc1cVd0pTMHUfxDElf0JPwLzq9jvhvXaa6/RpUsXgoODCQkJ4c8/rx5sVb21a9fyxhtvWCA6RblSQmZCuXpHaWnr2bP3Dop1F+nc6S1Cui+9IiEYMjPRp6Vh17l+isbF/pBAXmYRUfcGXbFusjkKcnM4sXsnHfvejHU9z7puyqrqU/gA+EAIMU1K+WE9xtQkxcbGsn79evbt24etrS3p6ekUFxfX+DgjRoxgxIgRFohQUf4hpSQ+I57b295euu100uckJPwPF5dwgrt9jI2NR7n9dCmmLr/6GIqaeiqLw9vP0X1ga5oHOFe/w1X+3rwRva5YPTqqIXPKXHwohOgqhLhTCDHx8ld9BNeUpKSk4OnpWVq7yNPTk5YtW+Lv78+zzz5LZGQkkZGRJCQkALBu3Tp69uxJaGgogwYNKi1+t3TpUh5//HHAtOjOf/7znwpLaStKbaTlp5Gjy6G9a3sAUlN/JCHhf3h73UZoyBcVJgSAs08+CYC2RfXzGmojO72AjQsP4ehiS+TwaxrrQsKuWJq3bY+Xn3/dBnedM3c5ziigM6aO4duA7cAXFo3sGv3888+kpqbW6TF9fHy47baqrzaGDBnCK6+8QocOHRg0aBB33XUXN998M2Cqnrpr1y6++OILnnzySdavX0+/fv3YuXMnQgg+//xz3nrrrdJV28qqqJS2otTWmRzTnNE2zm3Q63M4lfgRDg4BdO36wRWPi8qSxcXoziZj06YNdl0tN5InO72AH9/fj67QwMgnQ7GxM2+t6LIK83JJOXGc3mPusUCE1zdz/rbHAt2B/VLKB4QQzYHPLRtW0+Pk5MTevXvZtm0bW7Zs4a677irtG7jnnntK/3zqqacAOHv2LHfddRcpKSkUFxcTEFDx1VBFpbQVpbbO5ZqmA7VwbM7fh6ZRUJBESPcllSYEgKKTJ8FgwPM/0xCams0VMIeUkmM7U9n27XEQgpFPhtR45vJl547FgZS0VsNQa8ycpFAgpTQKIfQls5zPA20tHNc1q+6K3pKsrKyIiooiKiqKbt26sWzZMuDKctSXv582bRpPP/00I0aMICYmptK1nCsqpa0otXUuz5QURPZ2Ll3aRseO/4e7e9XLwhbGHQXALiioTmPJzSjk2J+pHI1NJTMtn5aBrgyc2AkXr+pLzVfmbNwhNFbW+AR2rMNIbwzmJIU9QghXTPMI9gK5wC6LRtUEHTt2DI1GQ2CgaXjfgQMHaNOmDX///TfffvstM2fO5Ntvv6V3796AqbS1b0ln3eXkoSj1JSU3BR97T5JOz8fVJQLfltU/ZinYvx9Ns2bYVHJXWxPFBXoS9p0nYU8aZ45mgISWga6E3daGjpE+NR5pdLWzcYfwaReI1sa2+sbKFcxZjvPyhLIFQohfAGcp5UHLhtX05ObmMm3aNDIzM7G2tqZ9+/Z89tlnrF+/nqKiInr27InRaOSbb74BYM6cOYwbNw5fX1969erFqVOnGvgTKDeSc3nnaN/MjeLiJNq3m2FW+YeC/fuwDw2p1aOj3IxCdq0/RfyuNPQ6I85e9oTf7k9QLx9cvOqmUJ2uqJC0kwmEDxtdJ8e70VQ1ea3SBW+EED2klGqllTLCwsL4448/Knzvscce46WXXrpi28iRIxk5cmS5tpMmTWLSpEmAaSRSWZcX2VGU2jqXe47e7i3ACFqtW7XtpdFIUeJpnKIGXNP5jAYjezYksj86CQl0jGxOp74taR7gXOf1iFLij2E0GGjVSfUnXIuq7hTKD4X5hwQG1nEsiqLUg0uFl0jJTaGlbxDkgbV19XMA8nfuBJ0O62sYipp5Pp8tXx7lXHwm7cO86TWqbZ3dFVTkbNxhEIKWHdWaHNeiqslr13ZJoFwhMTGxoUNQlCvsT9uPXurp6uZPrplJ4fx7c4GadzInH8vgp08OIoRgwIQgOvetWdnra5F89BDebdpi61Dx+uVK1cyZp1DhRDUpZaOcp6AoStVOZZ8CJCJ7G9bWzbCzq/oXtdTrKYqPx3XcOBx6mL+Metwf5/jty6O4ejsw4okQmrnb1TLy6hn0Os4dP0bwLUMtfq7rlTmjj8pWvrIDbgH20UgnrymKUrVTWae42dWJnOx9BHV8FWvrqqudFiedQRYVYR8aavY5Th64wJavjtE6yI0hD3bFzrF+ag+lnTyBvrgI3071u3b09cSc0UfTyr4WQrgAX1osIkVRLOpM1gnGOmXj5NSZli3vrLZ9UUI8ALaB7c06/unDF/n5079x83Gs14QApqGoAK2CVFK4VjWfPw75QP2srqEoSp3zNx7DURTRIXB2lTOYL0uf/zEAtm2rn7OamZbP5qVH8PB1Ysx/w9Da1mz9g9o6uW8Xnq3b4ODiWq/nvZ6Ys8jOOiHE2pKv9cAxTOWzq9tvsRDivBDiUCXvRwkhssoswPNizcNvHBITE+l6VS2YOXPm8M4771S53549e/jPf/4DQExMTKVDWqvi7+9Penp6ldv37t1LQEAA+/fvr9PS3DExMQwbNqxOjqXUj6zck/S2zybHpiNubtWvMSClpDgpCZv27dA4Vt1xezE5l5Vv7kFXaOCW+zvVe0LIuZhO8tEjdOzTv17Pe70x506h7G82PXBaSnnWjP2WAh9Rdd/DtsvLft6IwsPDCQ83LSQeExODk5MTffpUXWqgpg4ePMjYsWP59ttvCQ0NJTQ0VJXmvoHFxf8fANZed5vV/sJ7c5EFBbiMKD+npqyC3GI2fHIQK62GsTPDcfW23JDTypz++wAA7cLUgjq1YU7p7N+llL8D+4E4IF8I4W7GfluBS7UPsemLiooqLZ/doUMHtm3bBvxzpZ2YmMiCBQuYO3cuITfFMngAACAASURBVCEhbNu2jQsXLjBmzBgiIiKIiIhgx44dAFy8eJEhQ4YQGhrKlClTqqyHFBcXx6hRo/jyyy+JjDT9RzGnNLfRaOTRRx+lS5cuDBs2jNtvv730vV9++YWgoCD69evHDz/8UHquS5cuMWrUKP6/vTMPr6o6F/5v7eHMJ/MIIUwBggyCoEiLgkNxwFnbaievtrV2HvT2drjWWq/39rbf19tatWrb2362V1utU3vVWhUUJxQBmQKEQBISMicnyZnPHtb3x04CgQABEib373n2s/Zea+213x0477v2Gt539uzZnH322WzY4Gx6/+EPf8iNN97I0qVLmTBhAk899RTf/va3mTVrFhdffDGGYYzwX9vlQHR0LCceWcnLUZ0xOcPb2BVdsRwlK4vc6z9+wDrppMkzP1tHrCvNxbfMOi4GAWDXpvX4s7IpGDd0zGmX4TGcJam3AHcDScDGCbEpGRmneAuFEOuBJuB2KeXmo22wuvpuorEtRy/ZXoRD05k69Y6jasM0Td59912ef/557rrrLl5++eWBsgkTJnDrrbcSCoW4/fbbAfjEJz7BN7/5TRYtWsSuXbu46KKL2LJlC3fddReLFi3iBz/4Ac899xwPP/zwAZ955ZVX8sc//pFFixYdsM5Qrrmfeuop6urq2LhxI21tbUyfPp2bb76ZVCrF5z//eZYvX05FRQUf//geRXHnnXcyd+5cnnnmGZYvX85nPvMZ3n/f6bnt2LGDFStWUFVVxcKFC3nyySf5yU9+wtVXX81zzz3HVVdddVR/W5dDY9sG22v+HVMr5OXeGP8cOvR+AaOtjUzNDopuvw01a+i9DFJKlj+yhe6WBMu+PJvSydkjLfqwkFKya9N6ymfMHhUPrh8khjN89M/ADCnl/gPXR8daYLyUMiaEuBR4hgNMYPcZplsAysvLR1iMo+dA2/T3zr/mmmsAxx3GcDa0vfzyy1RVVQ1c9/b2Eo1GWbly5UAPfdmyZeTmHthFwYUXXshvfvMbLrroIlR16PHdoVxzv/HGG3z0ox9FURRKSko47zxnH+PWrVuZOHHigNO/T33qUwNG6Y033uDJJ58E4Pzzz6ezs5Oenh7A8Vyr6zqzZs3CsiwuvvhiAGbNmuVu7jtGtLe/SCJRy+7AMhTxOoX+wkPeE+/7og2cvfCAdVY9s5Od69r50DUVlM8YOjDPsaCrqZF4pIvyWacfNxlOFYZjFHbgrDgaUaSUvXudPy+EeEAIUTCU8ZFSPgw8DDB//vyD+o8+2h79kZCfn08kEhmU19XVNShGQr8LbFVVMU3zkG3ats3bb7+N37+/++Dh+oq57777uPXWW/nSl77EQw89NGSdoVxzH2xI6kDPHuqe/rr9z1AUBV3XB/IVRRnW38Ll6Gnc/Sg+3zi2pr2UBEtQlUNPAkdXrEAfOxbfjKHjMe9Y18baF+uZ/qFS5lw4bqRFPix2bVoPQPnMOcdVjlOB4XxnfRd4SwjxkBDi3v7jaB8shCgRfdpBCHFWnyydR9vu8SAUClFaWsorr7wCOAbh73//+0GHbfYlHA4TjUYHrpcuXcp99903cN0/FHPuuefyP//zP4ATZW5fY7Q3iqLw2GOPsW3bNn7wg+Ev7lq0aBFPPvkktm3T2trKq6++CkBlZSW1tbXs2LEDYMDj675yvfrqqxQUFJB1gCEHl2NLKt1Cd/c7jCm9jt2xJsaGDh1fObl5M7GXX8F32mlDdgS6WxO8+sdtFI0Ps/gT047a1fXRUr9hHVmFxeQUj26Y0A8Cw/lSeAhYDmzEmVMYFkKIx3DCeBYIIRqBOwEdQEr5IE5Ety8KIUyc+Yrr5UkcReaRRx7hy1/+MrfddhvgjLFPnjx52PdffvnlXHfddTz77LP88pe/5N577+XLX/4ys2fPxjRNzj33XB588EHuvPNObrjhBs444wwWL158yOE0r9fLs88+y+LFiykuLiZ4iGWFANdeey2vvPIKM2fOZOrUqSxYsIDs7Gx8Ph8PP/wwy5Yto6CggEWLFrFpk7Pi+Ic//CE33XQTs2fPJhAIuDEiTiA6O1YAoGWdycaOh/nsrM8e8p7ux58AIPuq/VcdmRmLFx7aiJSS82+cjqod3zH8TCpJ7bo1zLlo2XGV45RBSnnQA3jrUHWO5TFv3jy5L1VVVfvluRwd0WhUSillR0eHnDRpkmxubj5mz3b/PUeW99//nHzjzXPl8vrlcubvZ8q1rWsPeU/NRRfLXbd8Yb9827blK49Uyfu+8Iqs39QxGuIeNrXr3pP/52PLZO37a463KCc0wHtyGDp2OF8KK/omev8GpPcyJu5y01OYyy67jO7ubjKZDHfccQclJe5n+cmIZSXpirzJmDEf5/UeZ9ivIufg7iqM1jYydXXkfGx/Fxhb3mxmy5vNzLt4/HGdWN6bxq2bEYpyQrnKllKSTCZJpVJYloVlWWQyGUzTxDRNLMsinU5jmia2bWPb9kC9/jzLsgbK+o+KigpmzBhdFx7DMQqf6Eu/u1feSC1JdTlB6Z9HcDm5iUTexrbTFBRcQG3VCxT5iwh7wge9J/riiwCEzhk8J9bTnuT1J7ZTVpnLWVecOD//3VurKJ44GY/vyGM6g6PI+5VyMpkkHo+TTqeJx+MD+YlEgmQyiWmaZDIZenp6BhR9JpMhHo8PKPWjQVVVFEXZ78jLO+QWsaNmOA7xjj4gq4uLy3GhveMVVDVIbs5Z7Oy+n4k5h/45J99fh1ZYiHfKnhXiyViGvz+8EUXA+Z+ZjnKcJ5b7sUyD5ppqTjt/Ka2traRSKRKJBJlMBsMwSKfTGIZBJpMZOPrzE4kEpmkOXKdSqWE90+PxoOs6mqaRlZWFz+dD0zR0XScYDKJpGpqm4fV6CQQCqKqKqqp4PJ6BMlVV8Xq9aJo2oPD762nakbikGzlOmXgKUsoRD+vncuyRJ+9agxMOKW06OpaTl3cOQujU9tZy+aTLD3pP4r336H3+Bfzz5w3kxbvT/O2X6+luS7D0szOOSVyEA9HW1kZNTQ1VVVUkEglSySSJyTN5a1cLb/3qVwe8r19J67qOx+PB4/EQDofRdR1d1/F6vQPKXdO0gfL+tF9Z+3w+dP3YeX09HpwS8RR8Ph+dnZ3k5+e7huEkRkpJZ2cnPt/xUzqnEr2968lk2igqXEpboo24EWdSzsGHfWKvrQSg+J//GYCm7d28+OtNZFImy744m3Gnjf7wxb7E43FWrlxJdXX1wBJsn89HRUUFPS1NmA07WXTt9eQVFuL3+/H7/QMGoD9V3F3Ow+aUiKdQVlZGY2Mj7e3tx1sUl6PE5/NRVlZ2vMU4JWhv/wdCaOTnn8eaDmd3/KTsgxuFxJo1+E6fjX7aTN5+egfrXtpFVoGPK74+h/yxBw/GM5Kk02lqa2tZtWrVwK73nJwcFi1axLRp0xgzZgyqqvLE3d9DaHDueW704JHilIinoOv6oN3DLi4fdKSUtLW/SG7O2eh6Fju7dwIwMfvAvxMrFiO5YQOhT3+OZ362jtbaXqYuKGbx9dPw+Ed3nNuyLJqbm2loaKCuro7q6uqBocRp06Zx5plnUlExeNVUJpmgoWoTZ15x7ajK9kFjOHMKf8NZbQTOruPTgMdHUygXF5ejIxJ5m2SynokTHI+4tT21hPTQQX0eJd55h7gnn1Vts0mlolz8hZlMnls0KvIZhsH69evp6OigtrZ2wO8WOBO5FRUVTJ8+nenTpw/p6gWcVUfStimf4fo7GklGM56Ci4vLcWL37kfRtByKipYhpeSN3W9QmVd50Dm3zs31vD/7KyiKxrXfPp2i8SPnpiSdTrN9+3Z27txJbW3tIPcspaWlzJs3j7Fjx1JaWkpxcfGw5gDqN65D1XXGTKscMTldDmIUhBAVQLF0YinsnX+OEMIrpdwx6tK5uLgcNul0K+0d/2Bc2T+hql5a4600xhr51GmfOuA9PXWtvLw2G0v3c+VX51BYfvC9DMMhFouxZcsWqqqqqK2tHcgvLS1l4cKFTJo0iQkTJhzxap669esomz4T3esuTBhJDval8HPge0PkJ/vKDr62zcXF5biwu+lxpLQYO/YGAKoj1QBMy502ZH0pJSt+twFTD3D+xFoKyy89ouf29PTQ29s7YARaWloAZznorFmzmDJlCtOmTRvkmfdI6e1op7NxFzOXXHjUbbkM5mBGYYKUcsO+mVLK94QQE0ZNIhcXlyPGMHpobPx/5OWdQyDgTCr3G4UpuUOvD9n4aiO7W1WmNPyNKQ/832E9J5PJUF9fz44dO+jo6KC+vn5QFL2ioiLmz59PZWUlEyZMGPENWfUb1gEw4fQzRrRdl4MbhYN9kx3dfnIXF5dRYWftzzGMHiom/8tAXnWkmpJgCdne/aOipeIG7/6tlvxMI1MKIoiDDOUYhsHWrVvZsWMHGzZsGHDlkJubS0VFBWVlZWRnZ1NUVERR0ehMUPdTt34tobx88t3QmyPOwYzCaiHE56WUv947UwjxWWDN6Irl4uJyuESjm2ls/CNjx95AOLzHOVx1pJqpuVOHvGfdP+pJJ01mb/gDoZv2D4saiUSorq5mzZo1tLW1DeSXl5cza9Yspk+fTih07PYvANi2xa6N7zP5zLPdzaqjwMGMwjeAp4UQn2SPEZgPeICrR1swFxeX4WOaUbZs+S66nsvkSbcN5GesDHU9dSwZt2S/e2KRNOuXNzJpgkJ4RSOBefMBJ+rf2rVrWb169cBS0eLiYhYtWkRxcTHTpk3D4/Eck/caipaa7aTiMXfoaJQ4oFGQUrYCHxJCnAfM7Mt+Tkq5/JhI5uLiMiwMo5f31/8Tsfg2Zs16AF3fM0xU21OLKc0hvxTW/r0OKSXTMu+TBvRxZbz++uu8+eabpFIpiouLWbhwIXPnzqWgoOCEcRVRt34tCMH4WW7ozdFgOG4uVgArDrdhIcR/A5cBbVLKmUOUC+AXwKU4u6T/SUq59nCf4+LyQSaT6eT99TcRi1Uza+Z9FBZcMKi8f5J5X6NgGhbb3m1l8qxc0v/1MO0FBTz/pz8RjUaZPHkyc+bMYebMmSfc8IyUkpr3VlEyeQr+sBvudTQYzb3rvwfu48CO8y7BcZcxBVgA/KovdXFxGQax2DbWb7iFTKad2bN+RUHB/v5/Xmt8DY/iYXzW4AnZug2dZJImE3K76Q0GeH3pR/ALweWXX84ZZ5xxwhmDfmpWv0173U6W3vq14y3KKcuoGQUp5cpDLF29EnikL0zcKiFEjhCiVErZPFoyubicKnR0LGfT5m+gqkHmnfEnsrJm71dnZ89OXqx7kZtn3oymDP6p123owBfUSTS8y/9efjnYNrfeeCP5+SdGNLWhsG2LN/70B/LGlDHj3AsOfYPLEXE8BwnHAg17XTf25bm4uBwAKW3q6n7F+g23EAhM5Mwznx7SIACsaXXWh1wz5ZpB+Ubaon5TJ2OnZ/NyZye6ZbF06dIT2iAAVK1cQdfuBj58/adRVPV4i3PKcjxD/Az1fTpkhJW+GNG3gLMUzsXlg0gq1UxV1e1EuldRXHQZ06f/GFU98JahNa1ryPflUx4e/JupWdNGMp6hLvkWMY+HS8JhFnzoQ6Mt/lER6+rk9Ud/T8nkKUw568SW9WTneBqFRmDcXtdlQNNQFaWUDwMPA8yfP98NzeXygaO17QW2bv0+UhpMr/xPSkuvPei4vy1tVjWtYkHpgv3q7Xy/HT0vRXNnC3mdnUw799zRFv+o6GzcxZP/cSdGOs1HbvnqCTvfcapwPI3CX4GvCCH+hDPB3OPOJ7i4DMY0Y1Rvv5vm5r+QFZ7NjBn/RSAw4ZD3bevaRmeqk4VjFg7KNzIWDVVd2JNa0TsyLFnxKoHbbjtAK8cPyzRoqt7KxldeZOtbK/GHs/j4nf9B0YSDBwlyOXpGzSgIIR4DlgAFQohG4E5AB5BSPgg8j7MctQZnSepNoyWLi8vJSCy+nY0bv0giUceE8V9i4sSvoSiH9iiasTJ85/XvoAqVhaWDjUJLTQ+madIVbWByXR15F16IZ8KEUXqD4SOlpKupkfoN66jfsI6GzRsx0il0n58zLrmceZddTTiv4HiL+YFgNFcf3XCIcgl8ebSe7+JyMtPe/g82V92Oovg4Y+4fyc09e9j3PrrlUXb27OSeRfdQHCweVNZYHSGWvR1bSgo6Oij+6U9HWvRhI22bxq2b2fbWSnaufY9opxNON6eklNMWX8CE2XMpnzkbjz9w3GT8IHI8h49cXFz2QUqbnbW/oK7uPrLCs5k16wF8vtJh3/9209vcu+5eFpct5vJJg73bS1uyc0MzaX87PsNgfH4++thjv+BP2jZb31rJm4//kZ7WFjSvl4mnz+Psaz7O+NlzyC4qOeYyDYW0JTJlYmdssGykLcGWSBsYOJdgSaRtg81e13JPHasvlRKkdOpJ6SyrkTh5cq88nDpIiZR9z+or907Kxj8tb1Tf2zUKLi4nCIbRy+aqb9HZuYLS0uuYNvVHqOrwYw+sb1/P11d8nZJgCf969r/uNyHbWt9Lc2QXMttm0YpXKbz52I/YdjXt5u/3/4zmmm0Ujp/IpV+9nYr5Z6P7Dj9QjjRtrLiBHTOw4wbSsJCGjTRtRxH3pdKSjlK3pFNm2o7iNvfk2QkDmXQMwIAhMO1R+AscAQJQBAgQqnCNgovLB4He3g1s2vxNUqlGpk29i7FjP3lYq2xW7FrBv7z+LxT4C3jkkkco8O8//l6/sZNUoImseIK8ri78c+eO5CscFCklG195kRWP/BpN07nky9+i8sOLEbZAGhZWT3pAodsZR7nbCRM7lsHqU/oD5zEDK2YgU+bhCaEIhCpAUxCqQPSlaAqKX0PN86N5FBSfhvAoCF1F8fedqwpCEY5yVoRzrgqEwEkVpS8Ve56zd11FIBRAOMod0Xevss81e66dlGO+2so1Ci4uxxHbzlBbdz/19b/C4ylk7tw/kptz5mG18Ur9K9y+8nYmZU/i5+f9fEiDALBtQy2mHmNMTT0FX/gC/jNGxsuoFc2Q2dWL0Z7s63Fb2CkT2afczVSGaGs7eirDZWO/gM8XgpckTS+8dYCdSfsgQAloKEEPakhHHxPEF/KgBHWUkI4a8qAENYRHdRR934HqKGeh7qWwXQ6JaxRcXI4Tsdg2NlfdTixWRWnJNUyZcge6Pnwnb5Ztcf/79/Prjb9mRv4Mfr3014Q9Q8dWjkVS7EqsBw+UmCYFX7z1qHqg0rBJbOogsbaVdE33HuWuKSh+FcWngQbR7k56I+1Y0iCvfBy55eMQuur0vjWnNy50Za9jz7US0FFDOkpAd3reLscE1yi4uBxjbDtDXf1D1NXdj6ZlMXvWgxQWfuSw2miINvCvb/wra9vWcsXkK/juWd8l5DlwsJuqd3dh6r1UVNcz67LLUY5gDL+fVE2EyF+2Y3WnUXO9hM8bh68yD704gOJ1VEpj1Saev///EuvqZPqiJZx15UfJLxt3iJZdTgRco+DicgyJRFaxrfqHxOPbKS66jKlT78TjGd7EoWmbVHVW8edtf+aF2hfwql7uWXQPl0+6/KC9fikl615dCV44Y9okcj/5iSOWP/r6bnqe34lW4Kfg5pl4K3IGDctYpsFbTzzKu8/+hZyiEq6/6yeMmVp5xM9zOfa4RsHF5RiQSNSyvebHdHS8jM83ltNn/2ZIV9dJM0lHooO2ZBvtiXZaE600xZqo6qxia9dWUlaKgBbguqnXcfPMmykJHnz5prRtdn3nDjqUAvyGyvS7v+9Mih4BPS/VE31lF/6Z+eR+bBqKZ7BTuliki2d+8iNad9Yw87ylnHfj59w9BichrlFwcRlFpLSoq3uA2rr7EYqHUMmn6fbN4/nWBjrqfkZ7sp32ZPuAIYhmovu14df8VOZVct3U65hRMIPFZYsPOHew57mS2IpX6XzoIdakQxhzdCaMm3LEBiH2TjPRV3YRmF9M7jVT9pu0jbQ08fSP7yLW1ckVt33PdVp3EuMaBReXUcC0Td5reIGO+p/iN3ezKRXgz5020bongScB0BWdQn8hBYECJmRP4KzSsygKFFHoL6QwUEiRv4jCQCFZnqzDmhSOrVxJ+y/vI7VxI3g8VF15LWCz+KLFR/QuqZpuup/dgW9a7pAGoWNXHY/f/X2kbXPN9+6irHLGET3H5cTANQouLiPI9sh2Ht/6KNG2p1gcjCGAf6TL0LPP4fPlkynPKqcsVEZJsOSwlf2hsGIxmr/3faL/+AdKdjZ5N36Gniuuwnriz5SEJlNWVnbYbRrtCTr/ZwtaoZ+8Gyr3Mwh169fy3L0/RdN1PnrXf5I35vCf4XJi4RoFF5ejJGNleKn+JR7f9jg9Pav5aK5BSdjGDs5l7mk/Zlm4YvRlaGig4ZYvkKmtJWvZMkr//R4Ur5cVf/wLwtY4b/GFh92m2ZGk43ebEQoU3DjDWWa6F1vefI2/3/8z8seO44rbvk9OyfDdcbicuLhGwcXlCGmJt/D4tsd5cvuTqGYHHy/QqChK4/GUUFn5IwoLjk3IyNS2amqvvRZMk7IHHiB8vjOB3djYyNaaTehWFhNnFQ67PTtlElvVTO/LuxCqoPBzs9Dy9ixhlbbNykd/z3t/e4qy6TO56tt34A0ER/y9XI4PrlFwcTkMpJS80/IOf9r6J1Y0rCAgbG4aU8hkYaCqKhPG38a4cTejqke+D+BwSG3ZQt3Hr0fNzmbMf/4noUUfHpDzf//6HADjxkzA4xveTz29s5vOP23D7s3gq8wj9+oK1Ow9/pe6mnbzj4fuZffWzZy+dBlLPvM5NP3Q7rxdTh5co+ByShPLxHiz6U3ebX6X1a2rGRcex/0X3D+ojmVb9GR6iBtxUmbKOawUcSNOzIgRy8ToTnezs3snGzs20hhrZKwvzHcnTaHI3IK0Gxg79nomTvw6Xs+x8/mfrq2l7pOfQmYyjP3ZzwguOGugrLa2lpa2ZsLRCq79xrLhtVfXQ8fvNqPmeMn73Ey8k3MGzXm07qzhyX//AVJKlt76NWYu+YgbBe0UxDUKLqccUkrebnqbv2z/C2/sfoOkmSSgBUiYCWp7avndpt9R011DXU8dDdEGutPdyGE44RkbGktl7iS+Nr4cX+wN7Ew7xcVXMGHClwgGJx+DN9uD0dJC7TXXoni9jPvv3+KfM2egrL6+nkceeQRhq8yePYdgzqE9rfbPH6jZXgpvmY0a9gwq37VpPU//54/wZ2Xx0X/9N3JLj73LbZdjw6gaBSHExcAvABX4jZTyx/uULwGeBWr7sp6SUv5oNGVyObVZ2biS/1rzX9R011DoL+TSiZdyZcWVzCqYxWsNr/GNV7/Bz9b8jCJ/EROzJ3LB+AvI9+WT68sl7AnjU334NB8+1UdADxDSQ4Q8IcKeMHamg/UbPku8dztFxVcxceJXCAQmHvN3NCMR6q6/AZlMUvLv9wwYBNu22bJlK8889TRIhbA5jg9dOeWQ7Ulb0vWXahBQ8NmZ+xmE3du28MxP7ia7qJiP3nEPwZzcUXkvlxOD0QzHqQL3Ax8BGoHVQoi/Simr9qn6upTystGSw+WDQWO0kbtX3c1bTW8xMXsid5x9B1dVXIVH3aPgzis/jycuf4KSQAk5vpzDar+3dyPrN9yCbSeZM+f/kZ+3aKRfYViYkQh1H/s4VlcXZQ/cj/6hD9HQ0MCWTdvYsGETsWQ3iuWhMnwel33+zGF9JWTqesnU9ZJzdQVa7uC5kObt2/jL3d8nlJ/Pdd+/2zUIHwBG80vhLKBGSrkTQAjxJ+BKYF+j4OJyxEgp+UPVH7jv/ftQhcpX536Vm2bchK7uP/mpCIXKvMPzw2OaUerqH6Kh4bd49ALmnvFnQqFpIyX+ALZtY5ompmliGAamaZLJZEin06TTaVKpFInWVlof+xPR4iKS5y2he/UaEitXDrShmn7GeE/nnIvPpHLB2GGP9xvtCQB80wYr/N72Np756d0E8/K44Uc/JZB9eIbU5eRkNI3CWKBhr+tGYMEQ9RYKIdYDTcDtUsrNoyiTyynE2ta1/Hztz1nXto6J2RN58MIHGRMaMyJtW1aapubHqa29F8PooqT4SqZM+T4eT/4h700kErS2ttLb20tvby/RaJR4PD6g4NPpNJlMZpABsCxreIKNL0eVGkrch2L4CVqFhLOyOG32FGZ/eDI5RYfna0iaNol1bQi/hpq156sik0ryzE/vxsxk+NgP/sM1CCOBkYLG1dBdD71NkI5CJu4c9jADBk27BGZdN6pijqZRGKqbsu9s3lpgvJQyJoS4FHgG2G8QVAhxC3ALQHl5+UjL6XKSYdkWT1Q/wT3v3INAcMXkK/j2md8m25t95G1aKeKJGuKxbXRF3qa9/SUsK0ZOzgKmVHyXrKxZB7y3u7ubrVu30tDQQENDA729vYPKvV4voVAIj+5BVXUC3jAhjwpSAVvBNgXSAtsQ2AZYBlhpMFNgZwTC1hBSQ5EqKiqF5bmUTMyiZFI2xROzyCrwH/F797xYR6aul9zrpg7sVpZS8o8H76VjVz1Xf+fOD4zLayklScMinrboTRmYlsS0bWwbTNvGsiWmLfdKbQxLYloSw7LJWDamJcmYFhnLJmPamLbEl+pgTtNjzG77KwGze+B5hvCQUQJkFB+mcFRxf4hm2SdPf56UElvC7tQ4FpzERqER2Pt/UxnO18AAUsrevc6fF0I8IIQokFJ27FPvYeBhgPnz5w8nVpPLKUokFeEbK77B2ra1nFE0l/9z7o/J8fixrDjxeDuWlcA0Y5hWFNPsP2JY5t7X0X3Ko9h2auAZmhamqOhiSoqvJDd34ZDDMNFolHXr1rF582ZaW1sB8HsD5IWKKSyYhJIJYMV1rIRKpsvGzNiYgAmk92lLUQTeoIY/qOP1q2hKAtXoRDZsw9PZgF9JU3T+AsZ9/pMEcgMjFkHMaI0Te2M3wbNKCM4vHsh/95kn2Pb26yy64UYmzpk3Is8aSWxbSBUbIQAAGYFJREFU0pM0iKVN2qJpkhlHiScyFinDIp42SRqWo6hNR3GnTZu0aTmp4ZzH0yaJjEU8Y9KdMIgebnjPQ1BAD5/X/pdPqy/jJcNyOZ+n5BJqRRltSgG28KAgUKRAFaAKgaYqaIpAUwWqoqCrAlURhLwaAY/KJTNGf9f4aBqF1cAUIcREYDdwPTDIkbsQogRolVJKIcRZgAJ0jqJMLicJhtFDOt1COt1KOtNGOtVKTecaajrWsEQk+dSEMKr9LhveOWdY7SmKH00Lo6ohFBFCyCAqhahKEJ8eADuMIssQ5gRkspT4TsG2rTaWUYNpWFiGjWnYJFJxmmJb6UjXI7FRzSDB5EQ8qXw0K0AGEEGNcJ6PnDwvvnIdX1DHG+xLAxregIZup9C6W6GpHtncgLFrF6l3t2A0NCANA4QgcPYCsq6+mOwrLkcJjKwLajtt0fV4NcKrkXXRhIH89vpa3nz8j0xbeA5nXTm6PdJ9MS2bhGGRSFtEUwad8QydsQzNPUl2tMfY1ZVgW0uMSCKDZR+6b6gpAl1V8GgKuqrg1RS8uoJXU/HpCkGPRkHIS8CjkhPwkOXXCXhUgh6VLL+OriqoikBTBEpfqipikPJWFTHQvq4KPAr4mlbh3/IEWtXTYKURM6+DJd/hI/mTObxQSseHUTMKUkpTCPEV4EWcJan/LaXcLIS4ta/8QeA64ItCCBNIAtfL/m8mlw8Mphmlu2cNPT1rifZuJBrbSibTtl+9pAX5qpfirBkUhivweAr7FL0fVQ2g9KWqEiTV66G7BedohkhLhmhXCsuwDyFNGqgDQFEFqq6g6Qq2nqJb30FMtAKSHE8pEwtnUVRQRDjfTzjPRzjPRyjPO2j3sJ1Kka6uJrX5PVJvbyW1bSvJHTuJx2J7HqkoaCXF+KafRmjJEgJnzsc3YwZ6UdFR/22HQlo2XY9uwWiKkf+Z01CDzqS8bVu89PB9+IIhLvjsFw97Y5qUTo+8MZKkJ2mwoy1GWzRFd8KgI5amO+n0xpMZi1jaJJ42MSynJ29YzlDLgQh6VMblBfhwRT5luX7yg15CPo3CsJegRyPLrxH0aHg1haBXw6+rKMcyJrNlwupfw9sPQM8u8ISdsf8PfwMKRt/31UgiTjYdPH/+fPnee+8dbzFcjhLTjNLa+hwtrX+lp2cNUpoIoRIMTiEUqiSh5LG6fQevtaynNZ2iJLuSj1d+iismX4GqDA7uIqUk0pKgcWuEpuoITTXdJKMG4Cj2nOIAucUBwgV+/CEdX8jptXv8GpquoHkUNF1F1RUUDTJGmlQmSSIRp6Wlherqanbt2oWqqlRWVnLBBReQk5WFzGSwUynsWAwrEsGKRDAjEcyWVlJbt5KprSW9fTvYjiFSwmF8lZV4p1Sgl5fjKS/HO2kS+tixiGPkKkJKSeTJ7STeayXnmgpCZ+0ZjnjriUd5+y+PculXb2f6oiUHbce0bLa1RllbH2Fba5Tqlhg72mN0xjP71fVqCkVZXrL9Ojl+Dz5dJehVCXo1PH09eU0R+HW1r0wj6FUpCDn3lOX6yfLpx1bJHw6RenjiRmhaB+MXwfybYNql4DmxAgwJIdZIKecfsp5rFFyOJaYZo67uARp3/wHLShAITKawcCl5uR/CF5rOyw2v87cdf2NV8yq8qpeLJlzER6d+lNMLTx/Uc7VMm11VXex8v53GrV3EupyR+nCejzFTcyidnE3xxGxySwMYRoZIezs9O3fSu2sXsUiEeCxGIpMhadukpSQGZIQgLQTs00POjccpjkSYXrMDXzSKncmAYRz0PfVx4/CMH49/9iy8FRX4585FKypCqOpB7xtNrLhB99PbSW7qJHxBOdkfGT9Q1rqzhv/53reYvmgxl3zltoF8w7Jp6k5S0xajujXG9tYo21qjbG+LkTEdY6ergilFYSYWBplSFKIky0dJto+yXD/leUE82pEF9jkp2PwM/O1rzmzwFb+A067a7//PiYJrFFxOOCKRVWyuuo10upXi4ssoH3cz3sA03mx6k7/X/p03m96kN9NLSbCE66ddz3VTr9tvRVFHY4wtbzZR/W4rqbiBN6AxdmouxVMCePIN4ukeOjs76Y5EiDQ30xOLsX/fFYSUeDMZvKaJx7IIGgZeKfFJiV9K/EIQEAohVSFL1RA+H8LrQfF4EV6vc+71IjwelFAYNTcHLTcXNS8PNS8fNXTieA2VUpJc307333ZiJwzC55aRdfGEASMrpeSxH/wLHbsb0a//Htu6DGo74uzuTtERGzwtXpLlY0pxiOmlWVSWhJkxJpuJBae44h8KMw3L/w3e+iUUVsINj0LepOMt1UEZrlFwfR+5jDpSShoaf09NzX/g909g/rwnaLWDPLTtWZ7b+TU6U53keHNYMm4Jl0++nDPyz6C7uxuv9DphJXsTVL2zi63vNdDZ2o3UDUKlgkC2hSGTbOrp4Z3X4wPP8wCBaJRAby/lyRQ52VnklZeTPX482RMnkl1eTrC4GOUIQ1OeLNgpk/SObqKv7yZT14teFqLgc7PwlO4xWM09SR78/dNkVVexPH8xm5fXMybbx6TCEBdOz6I0209pto9JhUGmFIfJ9rseUWlaB89+BVo3wbybYOm/gTd0vKUaMdwvBZdRRUrJtuofsHv3o+TkncdO77k8teMFNnVuQlM05hXP45rSa8iJ57C7YTctLS10dXUNrNFWhIot99/YpaoqOTk55OTkkOX3E2hvx//22wQ2bMRv24TPP5+sSy8htGjRiK/cOVGRpk2mIUpqe4R0TTeZxijYoIR0spaOJzi/ZNBy1qfXNfK9J9bxsfpH0X1+zv7WPcwal0tx1rFx+31SYNsQa4WeBmjZCOv+4BgFfy5c/RBMveh4Szhs3C8Fl+OOlJKq6h/RsvtRtsoJ/HbDexjyHSrzKvnmvG9yWvo03nnjHd59610AQqEQRXkl5PvH0d2UpjcWQRUaRWPymDC9lDETCwiFQgSDQTwtLcRff4P4q6+ReO89ZDqNp2IyOV//OtmXLUMrHH5QmZMZadmktkVIrG0lVR1BZmwQ4BkXJrxkHL6KXDzlYcQ+wzt/eLuOO57dzLmxdWSZUT76ze9QfgzWwJ9w2Jazu7inAbp3QXeDs+O4/7qnEay9BiALp8MlP4HZHwf/qbnL2zUKLiOKYRts7tjMurZ1dDc/xmx1ByujGqsMlU/N+AwXll9IgVHA008/zd/b/05eTgFTx56B1ptHV51BtMb5QigYF+KcSz/M1LOK8QZ0jJYW4qtWkXh7Fa3vvIPZ0gKAd0oFOR/7GNmXLcM3e/YHxr+/0ZYg/m4LiffbsGMGSlAncEYxvik5eCfloPiH/mlLKXng1R389MVtXDTGpnLVGirPOY/ymacf4zc4xphp6KiG1s3OsE/rZuiscQzCvi4mgkWQUw6lp8P0yyF7HOSMh9wJUDDlhJ1IHilco+ByVBi2QVVnFatbVrO6ZTXr2taRNJMsCRlclWsQ0ady9cJ/54eFc+jtTLJy+Vs8VfUUGl6C0QkoLWV0C4WCcV5OW1REcZEgT3agttSQeeMlWh/ZSXrnzgEjoObkEDj7bIILFxI69xz00g9O71aaNsnNncTfaSa9swdUgb8yj8C8YnzTchHqwedIepIGv3h5O//9Zi2TC/ycvftZksEQS278/DF6g2OAbTnDPR3VznBP/9FRvUf5q14oqoRxCxzlnz3OSXPKIbsM9CN3G3Iq4BoFl8OiNd7Kuy3vsrlzM5s6NrG1aytpy1mhUpFTwTWTlzFP3YkSfYPCgkuYrNxBw4oefrPpBVrkJiwtgW5mMSU4h9Icm9zMbkJtW5Fra8g8VYcdi9Hv40QJBvFMmkRwwVl4K6cTXHg23qlTEaf4BHE/ViyD0ZrAaImT3tlDekcPMmWi5vnIungCwfnFqCHPIdup64jz5/caeHx1A53xDFfOGcO11npWr97Bsq9/G3846xi8zVEgpeM0LhkZfERboLcRenY7Pf7e3RBtHtzzzxoLxTMdR3LFM5zzvMmguqrvQLh/GZdD0h+X+LEtj/Fq46vY0sav+ZmeN52PTfsYpxeezvzi+WiZBjZu/BbpxC6Mjqt565mLSMm3SIWaSHnbEcAZCUHFa39B9Px6oH2jtBTvxAlkX3EFnsmT8E6ahGfSJGdd/0n2qS6lRBo2Mm0h0xZ2f5qxkGkTOzU4X2Ys7JS5X107YSKTe5SbmuMlMKsA38x8fFNyh/R/lMiY7GiL0xhJUNeZoLYjRk1bjHUN3UgJZ03M454PTyBctYJVT/+FWRdcxLSFw3MTckQku6F2JURqIdHpHEbS8fhnm31pvwfATN9hOEM9A3lpSMecekOheiFrjNPDH//hvvOxjuIvmQ3BQ3u1dRmMaxRcDsq2rm3c8eYdbOnaQo43h5tm3MQlEy9hcs5kNEXDNDPsqHqJ9Vu+ifS+SSaZTe36TxBNF2AWrMawnUm6iu01nP7++3hzcwmdvwT/3Dn4pp+Gd/KkE351kLQlVncaozWO0ZrAiqSwk6ZzpCxkynSUe5+S51CeNPoQHgXh1VC8KsKronhVlBwvuldF+DS0Aj96cQC9OIAS9uxnIC1b8n5DNyur23mtup2qpl4yprNSSyApCumMz/PzxQ+VcdWsIrSuRtb99VdsXr+WGYsv4MLPfWnkjW6sDdb9Eba/BA3vQP/KMdUD/jxnl6/qAUV3euuK7lx7QqB5Qe27Hjh08Iad1T6+HCf15zqTvOFSCOSf8mP8xxp3SarLkEgpue/9+3h4w8MU+gv56tyvcumkS7HSFnX179OyeznR6Gp8gRo0PYVheGhunkpjwwxsy0OWZZHXuJvc9nYmxWIUXXEFWcuW4Z065Zj2/qUtsWMZzK4UViSN2eUodGlYTo9+4Njr2rKRpgTTObfTFph7fidKQEMJ6Ch+DeHvU+oedUC59yt44VGRqqS3p514vJvennZ6I+1Eo12YVgrLtrBNE8sysU0L2zSwLAvbMrEtGyltpG07Xx/2nnPDtDBMC8u2QUoUaSOEYwjEIX7P/nAWZ115HfMuu3pk/x16m+DNe2HN7xyf3yWzYcpHoOIjUDLTUfqu8j6uuEtSXY6KJ6qf4OEND5PtyeaeynuIbG/lsbe+Rk72OsJZnaCA7vPTE5mItE+nIO/DzIjVMuv1Z/E3NKLn5BBasoSsm28iuGABwnPose8jQUqJTJqYXSnMSAqrK40ZSfUZgRRmJA3m4K678KhOL11TELriXGtOqgR0J08VoPXV8Sh9vfYgeqEfJXDwDVy2ZVG3fi1VK5ezc+1qjPQet9z+cBbB3DxUTUfRVFRNQ/f6UAMqiqajqiqKpqEoClIopEyb7qRJZ8KgN2XS1JMho0g0v0Jxtp+K4jDleSECPt1R8kJBURSEEAhFcQ4hUDWNvLJyxk6bju4doX0IsXbY8leoegbq3gAEnH4DnPMtyJ88Ms9wOea4XwougzBtk3vX3suza55lRnoGU6wc8vI2UlyyA11Pk4wWEGteSGnJR5iz6Dy8HbvofvIpep58EjuRILDwbPI++UlCixcftZM3KaUzLBM1sOKG0+PvTmN1Ocre6lP+Mj14c5vwa2h5PrRcL2qeDy3Xh5rrG8gT+sj6H5JSkrFsOlrb2PzqS9SsfJlkdxd6MEzOafPwTTgNGc7HCOSSQiNlWCQyFknDIpkZfJ7siwfQEUvTGc/Q//PUVcG4vADzynM5Z2ohS6YVkuU7DruLk92w6UnY/DTUvwnShvwpMOMqmPspZ9mmywmJ6/vI5bCwbZvNtZv57Wu/RWkWjA9HGTt2Ozk5uwFBrGkOvXUXMGPBxVSWpUm88hK9zz9PprYWdJ2siy8m79Ofwj979kGfI00bO25gxYy+NIMd61f6juLvP7dixn69fAChKwNKXs319il7H2R7SAY04oIB18zRvjSeNommTOJpi0TGJGPZjrtmSw5EzBrsxtnGMCWGfeB6pmkSTrSTH2ukPLGLcclGFCT1/nFsCp9GXWA8thjaAOmqwKerBDwqfl3F79Hw6woBj4bf43gILQx7KQh5mF2Ww+TCIOHjYQT6MVLw7kPw+v+FVM8eQzDjaig6zR0aOglwjYLLAZFS0tvbS2trK83NzVTvqmb3rt141SglxTsoKd2Bx5NAVfLoqV1My4azKRtXxky5DnP5c2R27HCCwMybR9aySwlfdBFaXt5A23Y0Q6YpjtmR7OvV9w3n9GSQB4pupQpEUEf6NUyfiuFVSHkUErpCXIFeBbqFpANJp7ToTVl0JzN0Jwx6kgY9CYNoeniRs7yagkcV+BQbvzDxSgufYuHBwoONBwtd2OhYaFh4pI2KicdMoRkJ9EwcNRVD625CGM5yXJFdiLfidLLnnEO4qAS/rvYpeAW/7ij6gEcdMAT6IfYUnFDsWAF//ZoTJ2DKUjjve1A6xzUEJxnunIIL4HwBdHV10dzcTHNzM7ubdtPc0kwm5awK8ngSBPLrqJixk9KsCCDICn2I7tpz2PraeIKazenNL5D9wj9ICIFv/pkErv0YqfnnEDG8dERSyOVtiGgjnu40gUgGb2ZP7z6jQI9XIaILIj7o9it0Iem0bTpsizbLYnfGoNO0oRfnOAgBj0rIqxHyaeQGPJRk+ZhWHCY7oJPt0wgrBn4riddMoaZjKKkoJKNYsR6MaA+p3giJjm4SPd1Ie5jLhPbCGwgSyM7Gn5tNwennUVY5g7GVM8gqHJ2AOMedDY/DU593vgxu/F+YOIpLWF1OCEb1S0EIcTHwC5zIa7+RUv54n3LRV34pkAD+SUq59mBtul8K+5PJZIhGowNHd3eErq5mOrqaiERasc0Uimqi6UnMQBfS24M/2ENxMEWe7kyCqhSi9i6hbdMZtDTmI6TF5OY3KO/eQqR4Kh1FU8kES8hVvJQgyGZwTzeFpBGbbVhsx6YaixZNYugqfo+Kz6PiUxXCqkWINAGZJkAGn7DxqxKfsPEqEo+w8QgbHRsNCx0bxTZRpYWw+1boGAaWaWAaBmYmQzoeI9HbQyoaRcohhpuEQiAnh2BObt+RRzAnB48/gO71ont9aF4vuteLqnvQNB3Vo6PpHufao6NqOt5gCO0YBcM5IWjdDL9dCiWz4NNPf+B3+p7sHPfhIyGEClQDHwEacWI23yClrNqrzqXAV3GMwgLgF1LKBQdr90QyCqZp0tXeTU8kRkd3J7FYL6lYG0aqA9vqBhFDkgQyQAYh0iAMhJIBYQIWQtiADcI5RN+5EHtSJ08650LuyR8431OmKBaKcugesJnMIh0Zj9kxDdE2A29sHEFFkCXThISF1+NHUfZ8SFoCYj6VVFDDDKnIgAJBifTZWB4DlAzCTCOMFDKdxEqnyCQTZJIJEj3d9Ha0E+1sx0zvG7b+4Kiahqp7UPV+Ja2hajqax9OXOsraH84ikJWNP5yFvy8NZDuGwJ+VhaIcv+A2JwxSOhPD0nbcQUjb2UcwcC2d61gbdO2EF7/nbDL7/HJnU5jLSc2JMHx0FlAjpdzZJ9CfgCuBqr3qXAk80heXeZUQIkcIUSqlbB5pYV559Fd0Nb8HAuc/vwAnXFLfIfrTviIkUuAoXgRSgC3AUm2kJ4nwJlE9SXQ93Xek0PUMgUM4TrQsFdvSsG0NKRWkFEipwECqIG3n2jnXkH3n+x22QEgV1dZQbRVVaqimB4/lRbV8CEtHWB4U04OwPehGGI+Rj9fIw8M+/t8DYNkGcTNKjxUhHo8QMyPEjG6iRoS4GcOWElsObxxZIPFqEq9q49dtCr0Wk/JNQh4bn2bj0yx8mo2uSFRFogon1YREFXZfXv/fv49B/Ze98yVEcY598w9Uf+hGD/+ew87nAPn7ds5G8Nn9hmCIr6iD4s2CG//mGoQPGKNpFMYCDXtdN+J8DRyqzlhgkFEQQtwC3AJQXl5+RMJ0Nb9H3txXj+jefZG2gm34EZkgwgijpkpRM2HUTBg9k4WSCaFlwqjpMKrpRbG8KKYXYWsoKMg+Q+TYwoErkHKvq/5yhszrv2avsv6a7Jfv5GVkhqSMYMpWLNJYZDDsOBl6MEUUoSbQVIGig+IR6ArkK4IiAYriQ1FAUUBVBB5N4NUFHl0ZdO7VFTy6QFPFXpuj9jIkgyYn9zEwByo77HwOkD+cdo722SOUP6RcR/oMBRTVSUVfqux9vneZcHYJ502E/ApnN7HLB4rRNApD/Y/etzs0nDpIKR8GHgZn+OhIhKlccAN1G8aDoiEUFaHoCFUHVUVRVISiogiBouoIVUHrz1P7Nhh5dDxeL95wLj5PHoqmoCgCRVFQNYGqCFRNRfQpQ9H/amJv5diftX8c4L3z9tzLwL2K6sjiyHoSrVxxcXE5qRhNo9AIjNvrugxoOoI6I8KsRRcya9GFo9G0i4uLyynDaHY5VwNThBAThRAe4Hrgr/vU+SvwGeFwNtAzGvMJLi4uLi7DY9S+FKSUphDiK8CLOEtS/1tKuVkIcWtf+YPA8zgrj2pwlqTeNFryuLi4uLgcmlHdvCalfB5H8e+d9+Be5xL48mjK4OLi4uIyfNwZSxcXFxeXAVyj4OLi4uIygGsUXFxcXFwGcI2Ci4uLi8sAJ53rbCFEO1B/vOVwcXFxOckYL6UsPFSlk84ouLi4uLiMHu7wkYuLi4vLAK5RcHFxcXEZwDUKLi4uLi4DuEbBxcXFxWUA1yi4uLi4uAzgGgUXFxcXlwFco+Di4uLiMoBrFFxcXFxcBnCNgouLi4vLAP8fA5lSH8gNLfoAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -383,6 +2572,13 @@ "plt.ylabel('Cumulative cases')\n", "plt.legend()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {